wehub-resource-sync
460 lines
20 KiB
Python
460 lines
20 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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# Copyright 2020 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 unittest
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import numpy as np
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from parameterized import parameterized
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from paddlenlp.transformers import ChatGLMTokenizer
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from paddlenlp.transformers.tokenizer_utils import PretrainedTokenizer
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from ...transformers.test_tokenizer_common import TokenizerTesterMixin
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VOCAB_FILES_NAMES = {
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"vocab_file": "ice_text.model",
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}
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class ChatGLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = ChatGLMTokenizer
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from_pretrained_vocab_key = "model_file"
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test_decode_token = True
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def get_tokenizer(self, **kwargs) -> PretrainedTokenizer:
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tokenizer = ChatGLMTokenizer.from_pretrained("THUDM/chatglm-6b", **kwargs)
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return tokenizer
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def get_input_output_texts(self, tokenizer):
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input_text = "lower newer"
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output_text = "lower newer"
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return input_text, output_text
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def test_full_tokenizer(self):
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tokenizer = self.get_tokenizer()
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text = "lower newer"
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bpe_tokens = ["▁lower", "▁newer"]
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tokens = tokenizer.tokenize(text, add_prefix_space=True)
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self.assertListEqual(tokens, bpe_tokens)
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input_tokens = tokens + [tokenizer.unk_token]
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input_bpe_tokens = [680, 10243, 0]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_pretokenized_inputs(self, *args, **kwargs):
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pass
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def test_tokenizers_common_ids_setters(self, *args, **kwargs):
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pass
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def test_mask_output(self):
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pass
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def test_offsets_mapping(self):
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pass
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def test_offsets_mapping_with_unk(self):
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pass
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def test_special_tokens_mask(self):
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pass
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def test_special_tokens_mask_input_pairs(self):
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pass
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def test_right_and_left_padding(self):
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pass
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def test_encode_decode_with_spaces(self):
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# TODO Fix decode in tokenizer.
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pass
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def test_add_special_tokens(self):
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pass
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def test_padding_to_multiple_of(self):
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pass
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def test_padding_side_in_kwargs(self):
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tokenizer = self.get_tokenizer(padding_side="left")
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self.assertEqual(tokenizer.padding_side, "left")
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tokenizer = self.get_tokenizer()
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self.assertEqual(tokenizer.padding_side, "left")
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def test_truncation_side_in_kwargs(self):
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tokenizer = self.get_tokenizer(truncation_side="left")
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self.assertEqual(tokenizer.truncation_side, "left")
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tokenizer = self.get_tokenizer(truncation_side="right")
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self.assertEqual(tokenizer.truncation_side, "right")
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def test_add_tokens(self):
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tokenizer = self.get_tokenizer()
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vocab_size = len(tokenizer)
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self.assertEqual(tokenizer.add_tokens(""), 0)
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self.assertEqual(tokenizer.add_tokens("testoken"), 1)
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self.assertEqual(tokenizer.add_tokens(["testoken1", "testtoken2"]), 2)
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self.assertEqual(len(tokenizer), vocab_size + 3)
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self.assertEqual(tokenizer.add_special_tokens({}), 0)
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self.assertRaises(AssertionError, tokenizer.add_special_tokens, {"additional_special_tokens": "<testtoken1>"})
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self.assertEqual(tokenizer.add_special_tokens({"additional_special_tokens": ["<testtoken2>"]}), 1)
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self.assertEqual(
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tokenizer.add_special_tokens({"additional_special_tokens": ["<testtoken3>", "<testtoken4>"]}), 2
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)
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self.assertIn("<testtoken3>", tokenizer.special_tokens_map["additional_special_tokens"])
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self.assertIsInstance(tokenizer.special_tokens_map["additional_special_tokens"], list)
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self.assertGreaterEqual(len(tokenizer.special_tokens_map["additional_special_tokens"]), 2)
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self.assertEqual(len(tokenizer), vocab_size + 6)
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def test_add_tokens_tokenizer(self):
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tokenizer = self.get_tokenizer()
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vocab_size = tokenizer.vocab_size
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all_size = len(tokenizer)
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self.assertNotEqual(vocab_size, 0)
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new_toks = ["aaaaa bbbbbb", "cccccccccdddddddd"]
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added_toks = tokenizer.add_tokens(new_toks)
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vocab_size_2 = tokenizer.vocab_size
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all_size_2 = len(tokenizer)
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self.assertNotEqual(vocab_size_2, 0)
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self.assertEqual(vocab_size, vocab_size_2)
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self.assertEqual(added_toks, len(new_toks))
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self.assertEqual(all_size_2, all_size + len(new_toks))
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tokens = tokenizer.encode(
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"aaaaa bbbbbb low cccccccccdddddddd l", return_token_type_ids=None, add_special_tokens=False
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)["input_ids"]
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self.assertGreaterEqual(len(tokens), 4)
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self.assertGreater(tokens[0], tokenizer.vocab_size - 1)
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self.assertGreater(tokens[-2], tokenizer.vocab_size - 1)
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def test_consecutive_unk_string(self):
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tokenizer = self.get_tokenizer(add_bos_token=False)
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tokens = [tokenizer.unk_token for _ in range(2)]
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string = tokenizer.convert_tokens_to_string(tokens)
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encoding = tokenizer(
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text=string,
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add_special_tokens=False,
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runcation=True,
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return_offsets_mapping=True,
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)
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# TODO (wanghuijuan): Aligned with transformers, but 2 expected.
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self.assertEqual(len(encoding["input_ids"]), 3)
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self.assertEqual(len(encoding["offset_mapping"]), 3)
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def test_padding_if_pad_token_set_slow(self):
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tokenizer = self.get_tokenizer()
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# Simple input
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s = "This is a simple input"
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s2 = ["This is a simple input looooooooong", "This is a simple input"]
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p = ("This is a simple input", "This is a pair")
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pad_token_id = tokenizer.pad_token_id
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out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np", return_attention_mask=True)
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out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np", return_attention_mask=True)
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out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np", return_attention_mask=True)
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# s
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# test single string max_length padding
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self.assertEqual(out_s["input_ids"].shape[-1], 30)
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self.assertTrue(pad_token_id in out_s["input_ids"])
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self.assertTrue(0 in out_s["attention_mask"][..., 0])
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# s2
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# test automatic padding
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self.assertEqual(out_s2["input_ids"].shape[-1], 11)
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# long slice doesn't have padding
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self.assertFalse(pad_token_id in out_s2["input_ids"][0])
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self.assertFalse(0 in out_s2["attention_mask"][0][..., 0])
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# short slice does have padding
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self.assertTrue(pad_token_id in out_s2["input_ids"][1])
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self.assertTrue(0 in out_s2["attention_mask"][1][..., 0])
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# p
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# test single pair max_length padding
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self.assertEqual(out_p["input_ids"].shape[-1], 60)
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self.assertTrue(pad_token_id in out_p["input_ids"])
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self.assertTrue(0 in out_p["attention_mask"][..., 0])
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def test_add_bos_token_slow(self):
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tokenizer = self.get_tokenizer()
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s = "This is a simple input"
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s2 = ["This is a simple input 1", "This is a simple input 2"]
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bos_token_id = tokenizer.bos_token_id
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out_s = tokenizer(s)
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out_s2 = tokenizer(s2)
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self.assertEqual(out_s.input_ids[-1], bos_token_id)
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self.assertTrue(all(o[-1] == bos_token_id for o in out_s2["input_ids"]))
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def test_pretrained_model_lists(self):
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# No max_model_input_sizes
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self.assertGreaterEqual(len(self.tokenizer_class.pretrained_resource_files_map), 1)
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self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_resource_files_map.values())[0]), 1)
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@parameterized.expand([(True,), (False,)])
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def test_encode_plus_with_padding(self, use_padding_as_call_kwarg: bool):
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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sequence = "Sequence"
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self._check_no_pad_token_padding(tokenizer, sequence)
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padding_size = 10
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padding_idx = tokenizer.pad_token_id
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token_type_padding_idx = tokenizer.pad_token_type_id
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encoded_sequence = tokenizer.encode(sequence, return_special_tokens_mask=True)
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input_ids = encoded_sequence["input_ids"]
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special_tokens_mask = encoded_sequence["special_tokens_mask"]
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sequence_length = len(input_ids)
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# Test right padding
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tokenizer_kwargs_right = {
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"max_length": sequence_length + padding_size,
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"padding": "max_length",
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"return_special_tokens_mask": True,
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}
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if not use_padding_as_call_kwarg:
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tokenizer.padding_side = "right"
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else:
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tokenizer_kwargs_right["padding_side"] = "right"
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self.assertRaises(AssertionError, lambda: tokenizer.encode_plus(sequence, **tokenizer_kwargs_right))
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# Test left padding
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tokenizer_kwargs_left = {
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"max_length": sequence_length + padding_size,
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"padding": "max_length",
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"return_special_tokens_mask": True,
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}
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if not use_padding_as_call_kwarg:
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tokenizer.padding_side = "left"
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else:
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tokenizer_kwargs_left["padding_side"] = "left"
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left_padded_sequence = tokenizer.encode_plus(sequence, **tokenizer_kwargs_left)
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left_padded_input_ids = left_padded_sequence["input_ids"]
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left_padded_special_tokens_mask = left_padded_sequence["special_tokens_mask"]
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left_padded_sequence_length = len(left_padded_input_ids)
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self.assertEqual(sequence_length + padding_size, left_padded_sequence_length)
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self.assertEqual([padding_idx] * padding_size + input_ids, left_padded_input_ids)
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self.assertEqual([1] * padding_size + special_tokens_mask, left_padded_special_tokens_mask)
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if "token_type_ids" in tokenizer.model_input_names:
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token_type_ids = encoded_sequence["token_type_ids"]
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left_padded_token_type_ids = left_padded_sequence["token_type_ids"]
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self.assertEqual(
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[token_type_padding_idx] * padding_size + token_type_ids, left_padded_token_type_ids
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)
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if "attention_mask" in tokenizer.model_input_names and "attention_mask" in encoded_sequence:
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attention_mask = encoded_sequence["attention_mask"]
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left_padded_attention_mask = left_padded_sequence["attention_mask"]
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self.assertEqual([0] * padding_size + attention_mask, left_padded_attention_mask)
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def test_padding_to_max_length(self):
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"""We keep this test for backward compatibility but it should be remove when `pad_to_max_seq_len` is deprecated."""
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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sequence = "Sequence"
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padding_size = 10
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# check correct behaviour if no pad_token_id exists and add it eventually
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self._check_no_pad_token_padding(tokenizer, sequence)
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padding_idx = tokenizer.pad_token_id
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# Check that it correctly pads when a maximum length is specified along with the padding flag set to True
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tokenizer.padding_side = "left"
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encoded_sequence = tokenizer.encode(sequence)["input_ids"]
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sequence_length = len(encoded_sequence)
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# FIXME: the next line should be padding(max_length) to avoid warning
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padded_sequence = tokenizer.encode(
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sequence, max_length=sequence_length + padding_size, pad_to_max_seq_len=True
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)["input_ids"]
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padded_sequence_length = len(padded_sequence)
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self.assertEqual(sequence_length + padding_size, padded_sequence_length)
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self.assertEqual([padding_idx] * padding_size + encoded_sequence, padded_sequence)
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# Check that nothing is done when a maximum length is not specified
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encoded_sequence = tokenizer.encode(sequence)["input_ids"]
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sequence_length = len(encoded_sequence)
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tokenizer.padding_side = "left"
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padded_sequence_left = tokenizer.encode(sequence, pad_to_max_seq_len=True)["input_ids"]
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padded_sequence_left_length = len(padded_sequence_left)
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self.assertEqual(sequence_length, padded_sequence_left_length)
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self.assertEqual(encoded_sequence, padded_sequence_left)
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def test_padding_with_attention_mask(self):
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tokenizers = self.get_tokenizers()
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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if tokenizer.pad_token is None:
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self.skipTest("No padding token.")
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if "attention_mask" not in tokenizer.model_input_names:
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self.skipTest("This model does not use attention mask.")
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features = [
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{"input_ids": [1, 2, 3], "attention_mask": np.array([[[0, 0, 0], [0, 0, 0], [0, 0, 1]]])},
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{
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"input_ids": [
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1,
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2,
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],
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"attention_mask": np.array([[[0, 0], [0, 1]]]),
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},
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]
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padded_features = tokenizer.pad(features)
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print(padded_features["attention_mask"])
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self.assertListEqual(
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[x.tolist() for x in padded_features["attention_mask"]],
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[
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[[[0, 0, 0], [0, 0, 0], [0, 0, 1]]],
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[[[0, 0, 0], [0, 0, 0], [0, 0, 1]]],
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],
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)
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def test_batch_encode_plus_padding(self):
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# Test that padded sequences are equivalent between batch_encode_plus and encode_plus
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# Left padding tests
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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tokenizer.padding_side = "left"
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sequences = [
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"Testing batch encode plus",
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"Testing batch encode plus with different sequence lengths",
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"Testing batch encode plus with different sequence lengths correctly pads",
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]
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max_length = 100
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# check correct behaviour if no pad_token_id exists and add it eventually
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self._check_no_pad_token_padding(tokenizer, sequences)
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encoded_sequences = [
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tokenizer.encode(sequence, max_length=max_length, padding="max_length") for sequence in sequences
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]
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encoded_sequences_batch = tokenizer.batch_encode(
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sequences, max_length=max_length, padding="max_length"
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)
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self.assertListEqual(
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[x["input_ids"] for x in encoded_sequences],
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[
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x["input_ids"]
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for x in self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)
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],
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)
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def test_batch_encode_plus_batch_sequence_length(self):
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# Tests that all encoded values have the correct size
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tokenizers = self.get_tokenizers(do_lower_case=False)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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sequences = [
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"Testing batch encode plus",
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"Testing batch encode plus with different sequence lengths",
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"Testing batch encode plus with different sequence lengths correctly pads",
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]
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def tolist(input_dict_list):
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if isinstance(input_dict_list, np.ndarray):
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return input_dict_list.tolist()
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unwrap = False
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if isinstance(input_dict_list, dict):
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input_dict_list = [input_dict_list]
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unwrap = True
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for i, input_dict in enumerate(input_dict_list):
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for k in input_dict:
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if isinstance(input_dict[k], np.ndarray):
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input_dict_list[i][k] = input_dict[k].tolist()
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return input_dict_list[0] if unwrap else input_dict_list
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encoded_sequences = [tokenizer.encode(sequence) for sequence in sequences]
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encoded_sequences_batch = tokenizer.batch_encode(sequences, padding=False)
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self.assertListEqual(
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tolist(encoded_sequences),
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tolist(self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch)),
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)
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maximum_length = len(
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max([encoded_sequence["input_ids"] for encoded_sequence in encoded_sequences], key=len)
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)
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# check correct behaviour if no pad_token_id exists and add it eventually
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self._check_no_pad_token_padding(tokenizer, sequences)
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encoded_sequences_padded = [
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tokenizer.encode(sequence, max_length=maximum_length, padding="max_length")
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for sequence in sequences
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]
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encoded_sequences_batch_padded = tokenizer.batch_encode(sequences, padding=True)
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self.assertListEqual(
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tolist(encoded_sequences_padded),
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tolist(self.convert_batch_encode_plus_format_to_encode_plus(encoded_sequences_batch_padded)),
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)
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# check 'longest' is unsensitive to a max length
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encoded_sequences_batch_padded_1 = tokenizer.batch_encode(sequences, padding=True)
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encoded_sequences_batch_padded_2 = tokenizer.batch_encode(
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sequences, max_length=maximum_length + 10, padding="longest"
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)
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for key in encoded_sequences_batch_padded_1.keys():
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self.assertListEqual(
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[x.tolist() for x in encoded_sequences_batch_padded_1[key]]
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if key != "input_ids"
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else encoded_sequences_batch_padded_1[key],
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[x.tolist() for x in encoded_sequences_batch_padded_2[key]]
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if key != "input_ids"
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else encoded_sequences_batch_padded_2[key],
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)
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# check 'no_padding' is unsensitive to a max length
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encoded_sequences_batch_padded_1 = tokenizer.batch_encode(sequences, padding=False)
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encoded_sequences_batch_padded_2 = tokenizer.batch_encode(
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sequences, max_length=maximum_length + 10, padding=False
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)
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for key in encoded_sequences_batch_padded_1.keys():
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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],
|
|
)
|