wehub-resource-sync
534 lines
25 KiB
Python
534 lines
25 KiB
Python
# Copyright (c) 2022 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 json
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import os
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import unittest
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from paddlenlp.transformers import DalleBartTokenizer
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from ..test_tokenizer_common import TokenizerTesterMixin
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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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"wiki_word_frequency_file": "enwiki-words-frequency.txt",
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}
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class TestTokenizationDalleBart(TokenizerTesterMixin, unittest.TestCase):
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tokenizer_class = DalleBartTokenizer
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test_rust_tokenizer = False
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test_offsets = 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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"<s>",
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"</s>",
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"<pad>",
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"<mask>",
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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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frequency = ["l 3123", "o 2133", "w 897", "r 1348", "e 6813", "s 7318", "t 1390"]
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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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self.wiki_word_frequency_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["wiki_word_frequency_file"])
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self.special_tokens_map = {
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"bos_token": "<s>",
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"eos_token": "</s>",
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"cls_token": "<s>",
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"sep_token": "</s>",
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"unk_token": "<unk>",
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"pad_token": "<pad>",
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"mask_token": "<mask>",
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}
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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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with open(self.wiki_word_frequency_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(frequency))
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def get_tokenizer(self, **kwargs):
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kwargs.update(self.special_tokens_map)
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return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs)
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def get_input_output_texts(self, tokenizer):
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return "lower newer", "lower newer"
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def test_call(self):
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# Tests that all call wrap to encode_plus and batch_encode_plus
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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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# Test not batched,should be processed before encode
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encoded_sequences_1 = tokenizer.encode(
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tokenizer.text_processor(sequences[0]),
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max_length=64, # default
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padding="max_length", # default
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truncation=True, # default)
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return_token_type_ids=False,
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return_attention_mask=True,
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)
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encoded_sequences_2 = tokenizer(sequences[0], return_token_type_ids=False, return_attention_mask=True)
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self.assertEqual(encoded_sequences_1, encoded_sequences_2)
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# Test not batched pairs
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encoded_sequences_1 = tokenizer.encode(
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tokenizer.text_processor(sequences[0]),
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tokenizer.text_processor(sequences[1]),
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max_length=64, # default
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padding="max_length", # default
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truncation=True, # default)
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return_token_type_ids=False,
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return_attention_mask=True,
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)
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encoded_sequences_2 = tokenizer(
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sequences[0], sequences[1], return_token_type_ids=False, return_attention_mask=True
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)
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self.assertEqual(encoded_sequences_1, encoded_sequences_2)
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# Test batched
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processed_seq = [tokenizer.text_processor(s) for s in sequences]
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encoded_sequences_1 = tokenizer.batch_encode(
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processed_seq,
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max_length=64, # default
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padding="max_length", # default
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truncation=True, # default)
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return_token_type_ids=False,
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return_attention_mask=True,
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)
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encoded_sequences_2 = tokenizer(sequences, return_token_type_ids=False, return_attention_mask=True)
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self.assertEqual(encoded_sequences_1, encoded_sequences_2)
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# Test batched pairs
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encoded_sequences_1 = tokenizer.batch_encode(
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list(zip(processed_seq, processed_seq)),
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max_length=64, # default
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padding="max_length", # default
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truncation=True, # default)
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return_token_type_ids=False,
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return_attention_mask=True,
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)
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encoded_sequences_2 = tokenizer(
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sequences, sequences, return_token_type_ids=False, return_attention_mask=True
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)
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self.assertEqual(encoded_sequences_1, encoded_sequences_2)
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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.encode(
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text=string, runcation=True, return_offsets_mapping=True, padding=False, truncation=False
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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"]), 2)
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def test_padding_to_multiple_of(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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else:
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empty_tokens = tokenizer.encode("", padding=True, pad_to_multiple_of=8)
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normal_tokens = tokenizer.encode("This is a sample input", padding=True, pad_to_multiple_of=8)
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for key, value in empty_tokens.items():
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self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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for key, value in normal_tokens.items():
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self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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normal_tokens = tokenizer.encode("This", pad_to_multiple_of=8)
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for key, value in normal_tokens.items():
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self.assertNotEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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# Should also work with truncation
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normal_tokens = tokenizer.encode("This", padding=True, truncation=True, pad_to_multiple_of=8)
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for key, value in normal_tokens.items():
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self.assertEqual(len(value) % 8, 0, f"BatchEncoding.{key} is not multiple of 8")
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# truncation to something which is not a multiple of pad_to_multiple_of raises an error
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self.assertRaises(
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ValueError,
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tokenizer.__call__,
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"This",
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padding=True,
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truncation=True,
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max_length=12,
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pad_to_multiple_of=8,
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)
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# __call__(),max_length default 64
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def test_maximum_encoding_length_pair_input(self):
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tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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# Build a sequence from our model's vocabulary
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stride = 2
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seq_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
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if len(ids) <= 2 + stride:
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seq_0 = (seq_0 + " ") * (2 + stride)
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ids = None
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seq0_tokens = tokenizer.encode(seq_0, return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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]
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self.assertGreater(len(seq0_tokens), 2 + stride)
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seq_1 = "This is another sentence to be encoded."
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seq1_tokens = tokenizer.encode(seq_1, return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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]
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if abs(len(seq0_tokens) - len(seq1_tokens)) <= 2:
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seq1_tokens = seq1_tokens + seq1_tokens
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seq_1 = tokenizer.decode(seq1_tokens, clean_up_tokenization_spaces=False)
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seq1_tokens = tokenizer.encode(seq_1, return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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]
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self.assertGreater(len(seq1_tokens), 2 + stride)
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# We are not using the special tokens - a bit too hard to test all the tokenizers with this
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# TODO try this again later
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sequence = tokenizer.encode(seq_0, seq_1, return_token_type_ids=None, add_special_tokens=False)[
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"input_ids"
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] # , add_prefix_space=False)
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# Test with max model input length
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model_max_length = tokenizer.model_max_length
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self.assertEqual(model_max_length, 100)
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seq_2 = seq_0 * model_max_length
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self.assertGreater(len(seq_2), model_max_length)
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sequence1 = tokenizer.encode(
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seq_1, return_token_type_ids=None, add_special_tokens=False, truncation=False
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)
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total_length1 = len(sequence1["input_ids"])
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sequence2 = tokenizer.encode(
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seq_2, seq_1, return_token_type_ids=None, add_special_tokens=False, truncation=False
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)
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total_length2 = len(sequence2["input_ids"])
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self.assertLess(
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total_length1, model_max_length - 10, "Issue with the testing sequence, please update it."
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)
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self.assertGreater(
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total_length2, model_max_length, "Issue with the testing sequence, please update it."
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)
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# Simple
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padding_strategies = (
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[False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
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)
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for padding_state in padding_strategies:
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with self.subTest(f"{tokenizer.__class__.__name__} Padding: {padding_state}"):
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for truncation_state in [True, "longest_first", "only_first"]:
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with self.subTest(f"{tokenizer.__class__.__name__} Truncation: {truncation_state}"):
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output = tokenizer.encode(
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seq_2,
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seq_1,
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padding=padding_state,
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truncation=truncation_state,
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max_length=model_max_length,
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)
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self.assertEqual(len(output["input_ids"]), model_max_length)
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output = tokenizer(
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[seq_2],
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[seq_1],
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padding=padding_state,
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truncation=truncation_state,
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max_length=model_max_length,
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)
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self.assertEqual(len(output["input_ids"][0]), model_max_length)
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# Simple
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output = tokenizer.encode(
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seq_1, seq_2, padding=padding_state, truncation="only_second", max_length=model_max_length
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)
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self.assertEqual(len(output["input_ids"]), model_max_length)
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output = tokenizer(
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[seq_1],
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[seq_2],
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padding=padding_state,
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truncation="only_second",
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max_length=model_max_length,
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)
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self.assertEqual(len(output["input_ids"][0]), model_max_length)
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# Simple with no truncation
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# Reset warnings
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tokenizer.deprecation_warnings = {}
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with self.assertLogs("PaddleNLP", level="WARNING") as cm:
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output = tokenizer.encode(seq_1, seq_2, padding=padding_state, truncation=False)
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self.assertNotEqual(len(output["input_ids"]), model_max_length)
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self.assertEqual(len(cm.records), 1)
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self.assertTrue(
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cm.records[0].message.startswith(
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"Token indices sequence length is longer than the specified maximum sequence length for this model"
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)
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)
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tokenizer.deprecation_warnings = {}
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with self.assertLogs("PaddleNLP", level="WARNING") as cm:
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output = tokenizer(
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[seq_1], [seq_2], padding=padding_state, max_length=None, truncation=False
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)
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self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
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self.assertEqual(len(cm.records), 1)
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self.assertTrue(
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cm.records[0].message.startswith(
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"Token indices sequence length is longer than the specified maximum sequence length for this model"
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)
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)
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truncated_first_sequence = (
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tokenizer.encode(seq_0, return_token_type_ids=None, add_special_tokens=False)["input_ids"][:-2]
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+ tokenizer.encode(seq_1, return_token_type_ids=None, add_special_tokens=False)["input_ids"]
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)
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truncated_second_sequence = (
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tokenizer.encode(seq_0, return_token_type_ids=None, add_special_tokens=False)["input_ids"]
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+ tokenizer.encode(seq_1, return_token_type_ids=None, add_special_tokens=False)["input_ids"][:-2]
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)
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# TODO(wj-Mcat): `overflow_first_sequence` and `overflow_second_sequence` is not used
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# to make CI green, the following codes will be commented out
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# overflow_first_sequence = (
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# tokenizer.encode(seq_0, return_token_type_ids=None, add_special_tokens=False)["input_ids"][
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# -(2 + stride) :
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# ]
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# + tokenizer.encode(seq_1, return_token_type_ids=None, add_special_tokens=False)["input_ids"]
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# )
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# overflow_second_sequence = (
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# tokenizer.encode(seq_0, return_token_type_ids=None, add_special_tokens=False)["input_ids"]
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# + tokenizer.encode(seq_1, return_token_type_ids=None, add_special_tokens=False)["input_ids"][
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# -(2 + stride) :
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# ]
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# )
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with self.assertRaises(ValueError) as context:
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tokenizer.encode(
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seq_0,
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seq_1,
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max_length=len(sequence) - 2,
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return_token_type_ids=None,
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add_special_tokens=False,
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stride=stride,
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truncation="longest_first",
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return_overflowing_tokens=True,
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# add_prefix_space=False,
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)
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self.assertTrue(
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context.exception.args[0].startswith(
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"Not possible to return overflowing tokens for pair of sequences with the "
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"`longest_first`. Please select another truncation strategy than `longest_first`, "
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"for instance `only_second` or `only_first`."
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)
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)
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# Overflowing tokens are handled quite differently in slow and fast tokenizers
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# No overflowing tokens when using 'longest' in python tokenizers
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with self.assertRaises(ValueError) as context:
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tokenizer.encode(
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seq_0,
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seq_1,
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max_length=len(sequence) - 2,
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return_token_type_ids=None,
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add_special_tokens=False,
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stride=stride,
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truncation=True,
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return_overflowing_tokens=True,
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# add_prefix_space=False,
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)
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self.assertTrue(
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context.exception.args[0].startswith(
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"Not possible to return overflowing tokens for pair of sequences with the "
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"`longest_first`. Please select another truncation strategy than `longest_first`, "
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"for instance `only_second` or `only_first`."
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)
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)
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information_first_truncated = tokenizer.encode(
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seq_0,
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seq_1,
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max_length=len(sequence) - 2,
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return_token_type_ids=None,
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add_special_tokens=False,
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stride=stride,
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truncation="only_first",
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return_overflowing_tokens=True,
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# add_prefix_space=False,
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)
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# Overflowing tokens are handled quite differently in slow and fast tokenizers
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truncated_sequence = information_first_truncated["input_ids"]
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overflowing_tokens = information_first_truncated["overflowing_tokens"]
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self.assertEqual(len(truncated_sequence), len(sequence) - 2)
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self.assertEqual(truncated_sequence, truncated_first_sequence)
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self.assertEqual(len(overflowing_tokens), 2 + stride)
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self.assertEqual(overflowing_tokens, seq0_tokens[-(2 + stride) :])
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information_second_truncated = tokenizer.encode(
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seq_0,
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seq_1,
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max_length=len(sequence) - 2,
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return_token_type_ids=None,
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add_special_tokens=False,
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stride=stride,
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truncation="only_second",
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return_overflowing_tokens=True,
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# add_prefix_space=False,
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)
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# Overflowing tokens are handled quite differently in slow and fast tokenizers
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truncated_sequence = information_second_truncated["input_ids"]
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overflowing_tokens = information_second_truncated["overflowing_tokens"]
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self.assertEqual(len(truncated_sequence), len(sequence) - 2)
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self.assertEqual(truncated_sequence, truncated_second_sequence)
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self.assertEqual(len(overflowing_tokens), 2 + stride)
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self.assertEqual(overflowing_tokens, seq1_tokens[-(2 + stride) :])
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# __call__(),max_length default 64
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def test_maximum_encoding_length_single_input(self):
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tokenizers = self.get_tokenizers(do_lower_case=False, model_max_length=100)
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for tokenizer in tokenizers:
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with self.subTest(f"{tokenizer.__class__.__name__}"):
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seq_0, ids = self.get_clean_sequence(tokenizer, max_length=20)
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sequence = tokenizer.encode(seq_0, return_token_type_ids=None, add_special_tokens=False)["input_ids"]
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total_length = len(sequence)
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self.assertGreater(total_length, 4, "Issue with the testing sequence, please update it it's too short")
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# Test with max model input length
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model_max_length = tokenizer.model_max_length
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self.assertEqual(model_max_length, 100)
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seq_1 = seq_0 * model_max_length
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sequence1 = tokenizer.encode(
|
|
seq_1, return_token_type_ids=None, add_special_tokens=False, truncation=False
|
|
)
|
|
total_length1 = len(sequence1["input_ids"])
|
|
self.assertGreater(
|
|
total_length1, model_max_length, "Issue with the testing sequence, please update it it's too short"
|
|
)
|
|
|
|
# Simple
|
|
padding_strategies = (
|
|
[False, True, "longest"] if tokenizer.pad_token and tokenizer.pad_token_id >= 0 else [False]
|
|
)
|
|
for padding_state in padding_strategies:
|
|
with self.subTest(f"Padding: {padding_state}"):
|
|
for truncation_state in [True, "longest_first", "only_first"]:
|
|
with self.subTest(f"Truncation: {truncation_state}"):
|
|
output = tokenizer.encode(seq_1, padding=padding_state, truncation=truncation_state)
|
|
self.assertEqual(len(output["input_ids"]), model_max_length)
|
|
|
|
output = tokenizer(
|
|
[seq_1],
|
|
padding=padding_state,
|
|
max_length=model_max_length,
|
|
truncation=truncation_state,
|
|
)
|
|
self.assertEqual(len(output["input_ids"][0]), model_max_length)
|
|
|
|
# Simple with no truncation
|
|
# Reset warnings
|
|
tokenizer.deprecation_warnings = {}
|
|
with self.assertLogs("PaddleNLP", level="WARNING") as cm:
|
|
output = tokenizer.encode(seq_1, padding=padding_state, truncation=False)
|
|
self.assertNotEqual(len(output["input_ids"]), model_max_length)
|
|
self.assertEqual(len(cm.records), 1)
|
|
self.assertTrue(
|
|
cm.records[0].message.startswith(
|
|
"Token indices sequence length is longer than the specified maximum sequence length for this model"
|
|
)
|
|
)
|
|
|
|
tokenizer.deprecation_warnings = {}
|
|
with self.assertLogs("PaddleNLP", level="WARNING") as cm:
|
|
output = tokenizer([seq_1], padding=padding_state, truncation=False, max_length=None)
|
|
self.assertNotEqual(len(output["input_ids"][0]), model_max_length)
|
|
self.assertEqual(len(cm.records), 1)
|
|
self.assertTrue(
|
|
cm.records[0].message.startswith(
|
|
"Token indices sequence length is longer than the specified maximum sequence length for this model"
|
|
)
|
|
)
|
|
|
|
# Overflowing tokens
|
|
stride = 2
|
|
information = tokenizer.encode(
|
|
seq_0,
|
|
max_length=total_length - 2,
|
|
return_token_type_ids=None,
|
|
add_special_tokens=False,
|
|
stride=stride,
|
|
truncation="longest_first",
|
|
return_overflowing_tokens=True,
|
|
# add_prefix_space=False,
|
|
)
|
|
|
|
# Overflowing tokens are handled quite differently in slow and fast tokenizers
|
|
|
|
truncated_sequence = information["input_ids"]
|
|
overflowing_tokens = information["overflowing_tokens"]
|
|
|
|
self.assertEqual(len(truncated_sequence), total_length - 2)
|
|
self.assertEqual(truncated_sequence, sequence[:-2])
|
|
|
|
self.assertEqual(len(overflowing_tokens), 2 + stride)
|
|
self.assertEqual(overflowing_tokens, sequence[-(2 + stride) :])
|