wehub-resource-sync
236 lines
8.3 KiB
Python
236 lines
8.3 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. 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 json
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import os
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import unittest
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from paddlenlp.transformers.glm.tokenizer import GLMBertTokenizer, GLMGPT2Tokenizer
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from ..test_tokenizer_common import TokenizerTesterMixin, filter_non_english
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VOCAB_FILES_NAMES = {
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"vocab_file": "vocab.json",
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"merges_file": "merges.txt",
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}
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class GLMBertTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = GLMBertTokenizer
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space_between_special_tokens = True
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from_pretrained_filter = filter_non_english
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test_seq2seq = True
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def setUp(self):
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super().setUp()
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vocab_tokens = [
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"[UNK]",
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"[CLS]",
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"[SEP]",
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"[PAD]",
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"[MASK]",
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"want",
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"##want",
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"##ed",
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"wa",
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"un",
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"runn",
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"##ing",
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",",
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"low",
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"lowest",
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]
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self.special_tokens_map = {"truncation_side": "right"}
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self.vocab_file = os.path.join(self.tmpdirname, GLMBertTokenizer.resource_files_names["vocab_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
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vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
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def get_input_output_texts(self, tokenizer):
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input_text = "UNwant\u00E9d,running"
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output_text = "unwanted, running"
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return input_text, output_text
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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return GLMBertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def test_full_tokenizer(self):
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tokenizer = self.tokenizer_class(self.vocab_file, **self.special_tokens_map)
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tokens = tokenizer.tokenize("UNwant\u00E9d,running")
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self.assertListEqual(tokens, ["un", "##want", "##ed", ",", "runn", "##ing"])
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self.assertListEqual(tokenizer.convert_tokens_to_ids(tokens), [9, 6, 7, 12, 10, 11])
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class GLMGPT2TokenizerTest(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = GLMGPT2Tokenizer
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from_pretrained_kwargs = {"add_prefix_space": True}
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test_seq2seq = False
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def setUp(self):
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super().setUp()
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vocab = [
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"l",
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"o",
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"w",
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"e",
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"r",
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"s",
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"t",
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"i",
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"d",
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"n",
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"\u0120",
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"\u0120l",
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"\u0120n",
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"\u0120lo",
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"\u0120low",
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"er",
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"\u0120lowest",
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"\u0120newer",
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"\u0120wider",
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"<unk>",
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"<|endoftext|>",
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"[CLS]",
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]
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
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self.special_tokens_map = {"unk_token": "<unk>", "truncation_side": "right"}
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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with open(self.merges_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(merges))
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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return GLMGPT2Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
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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_pretokenized_inputs(self, *args, **kwargs):
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pass
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def test_full_tokenizer(self):
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tokenizer = GLMGPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map)
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text = "lower newer"
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bpe_tokens = ["\u0120low", "er", "\u0120", "n", "e", "w", "er"]
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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 = [14, 15, 10, 9, 3, 2, 15, 19]
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self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens)
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def test_offsets_mapping(self):
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if not self.test_offsets:
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return
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for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
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with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
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tokenizer = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs)
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text = "Wonderful no inspiration example with subtoken"
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# No pair
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tokens_with_offsets = tokenizer.encode(
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text, return_special_tokens_mask=True, return_offsets_mapping=True, add_special_tokens=True
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)
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added_tokens = tokenizer.num_special_tokens_to_add(False)
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offsets = tokens_with_offsets["offset_mapping"]
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print(offsets)
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print(added_tokens)
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print(tokens_with_offsets["input_ids"], tokenizer.decode(tokens_with_offsets["input_ids"]))
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# Assert there is the same number of tokens and offsets
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self.assertEqual(len(offsets), len(tokens_with_offsets["input_ids"]))
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# Assert there is online added_tokens special_tokens
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self.assertEqual(sum(tokens_with_offsets["special_tokens_mask"]), added_tokens)
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def test_padding_different_model_input_name(self):
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pass
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def test_padding_if_pad_token_set_slow(self):
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tokenizer = GLMGPT2Tokenizer.from_pretrained(self.tmpdirname, pad_token="<pad>")
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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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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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# 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"])
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# s2
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# test automatic padding
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self.assertEqual(out_s2["input_ids"].shape[-1], 35)
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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])
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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])
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def test_add_bos_token_slow(self):
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pass
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def test_maximum_encoding_length_pair_input(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_number_of_added_tokens(self):
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pass
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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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def test_consecutive_unk_string(self):
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tokenizers = self.get_tokenizers(fast=True, do_lower_case=True)
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for tokenizer in tokenizers:
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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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truncation=True,
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return_offsets_mapping=True,
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)
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self.assertEqual(len(encoding["input_ids"]), 4)
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self.assertEqual(len(encoding["offset_mapping"]), 4)
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if __name__ == "__main__":
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unittest.main()
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