From 0f257a87749e0a72bda260c6f319a45dae1e7c4d Mon Sep 17 00:00:00 2001 From: tgadeliya <32731151+tgadeliya@users.noreply.github.com> Date: Mon, 22 Aug 2022 12:13:20 +0200 Subject: [PATCH] Add missing tokenizer tests - Longformer (#17677) --- .../test_tokenization_longformer.py | 305 ++++++++++++++++++ 1 file changed, 305 insertions(+) create mode 100644 tests/models/longformer/test_tokenization_longformer.py diff --git a/tests/models/longformer/test_tokenization_longformer.py b/tests/models/longformer/test_tokenization_longformer.py new file mode 100644 index 0000000000..2397a40baf --- /dev/null +++ b/tests/models/longformer/test_tokenization_longformer.py @@ -0,0 +1,305 @@ +# coding=utf-8 +# Copyright 2022 Tsimur Hadeliya. 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. +""" Testing suite for the Longformer tokenizer. """ + + +import itertools +import json +import os +import unittest + +from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast +from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES +from transformers.testing_utils import require_tokenizers, slow + +from ...test_tokenization_common import TokenizerTesterMixin + + +# Copied from transformers.tests.roberta.test_modeling_roberta.py with Roberta->Longformer +@require_tokenizers +class LongformerTokenizationTest(TokenizerTesterMixin, unittest.TestCase): + tokenizer_class = LongformerTokenizer + test_slow_tokenizer = True + rust_tokenizer_class = LongformerTokenizerFast + test_rust_tokenizer = True + + def setUp(self): + super().setUp() + + # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt + vocab = [ + "l", + "o", + "w", + "e", + "r", + "s", + "t", + "i", + "d", + "n", + "\u0120", + "\u0120l", + "\u0120n", + "\u0120lo", + "\u0120low", + "er", + "\u0120lowest", + "\u0120newer", + "\u0120wider", + "", + ] + vocab_tokens = dict(zip(vocab, range(len(vocab)))) + merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] + self.special_tokens_map = {"unk_token": ""} + + self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) + self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) + with open(self.vocab_file, "w", encoding="utf-8") as fp: + fp.write(json.dumps(vocab_tokens) + "\n") + with open(self.merges_file, "w", encoding="utf-8") as fp: + fp.write("\n".join(merges)) + + def get_tokenizer(self, **kwargs): + kwargs.update(self.special_tokens_map) + return self.tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) + + def get_rust_tokenizer(self, **kwargs): + kwargs.update(self.special_tokens_map) + return self.rust_tokenizer_class.from_pretrained(self.tmpdirname, **kwargs) + + 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.tokenizer_class(self.vocab_file, self.merges_file, **self.special_tokens_map) + text = "lower newer" + bpe_tokens = ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"] + tokens = tokenizer.tokenize(text) # , add_prefix_space=True) + self.assertListEqual(tokens, bpe_tokens) + + input_tokens = tokens + [tokenizer.unk_token] + input_bpe_tokens = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19] + self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) + + def longformer_dict_integration_testing(self): + tokenizer = self.get_tokenizer() + + self.assertListEqual(tokenizer.encode("Hello world!", add_special_tokens=False), [0, 31414, 232, 328, 2]) + self.assertListEqual( + tokenizer.encode("Hello world! cécé herlolip 418", add_special_tokens=False), + [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2], + ) + + @slow + def test_sequence_builders(self): + tokenizer = self.tokenizer_class.from_pretrained("allenai/longformer-base-4096") + + text = tokenizer.encode("sequence builders", add_special_tokens=False) + text_2 = tokenizer.encode("multi-sequence build", add_special_tokens=False) + + encoded_text_from_decode = tokenizer.encode( + "sequence builders", add_special_tokens=True, add_prefix_space=False + ) + encoded_pair_from_decode = tokenizer.encode( + "sequence builders", "multi-sequence build", add_special_tokens=True, add_prefix_space=False + ) + + encoded_sentence = tokenizer.build_inputs_with_special_tokens(text) + encoded_pair = tokenizer.build_inputs_with_special_tokens(text, text_2) + + assert encoded_sentence == encoded_text_from_decode + assert encoded_pair == encoded_pair_from_decode + + def test_space_encoding(self): + tokenizer = self.get_tokenizer() + + sequence = "Encode this sequence." + space_encoding = tokenizer.byte_encoder[" ".encode("utf-8")[0]] + + # Testing encoder arguments + encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=False) + first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0] + self.assertNotEqual(first_char, space_encoding) + + encoded = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True) + first_char = tokenizer.convert_ids_to_tokens(encoded[0])[0] + self.assertEqual(first_char, space_encoding) + + tokenizer.add_special_tokens({"bos_token": ""}) + encoded = tokenizer.encode(sequence, add_special_tokens=True) + first_char = tokenizer.convert_ids_to_tokens(encoded[1])[0] + self.assertNotEqual(first_char, space_encoding) + + # Testing spaces after special tokens + mask = "" + tokenizer.add_special_tokens( + {"mask_token": AddedToken(mask, lstrip=True, rstrip=False)} + ) # mask token has a left space + mask_ind = tokenizer.convert_tokens_to_ids(mask) + + sequence = "Encode sequence" + sequence_nospace = "Encode sequence" + + encoded = tokenizer.encode(sequence) + mask_loc = encoded.index(mask_ind) + first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0] + self.assertEqual(first_char, space_encoding) + + encoded = tokenizer.encode(sequence_nospace) + mask_loc = encoded.index(mask_ind) + first_char = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0] + self.assertNotEqual(first_char, space_encoding) + + def test_pretokenized_inputs(self): + pass + + def test_embeded_special_tokens(self): + for tokenizer, pretrained_name, kwargs in self.tokenizers_list: + with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): + tokenizer_r = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs) + tokenizer_p = self.tokenizer_class.from_pretrained(pretrained_name, **kwargs) + sentence = "A, AllenNLP sentence." + tokens_r = tokenizer_r.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) + tokens_p = tokenizer_p.encode_plus(sentence, add_special_tokens=True, return_token_type_ids=True) + + # token_type_ids should put 0 everywhere + self.assertEqual(sum(tokens_r["token_type_ids"]), sum(tokens_p["token_type_ids"])) + + # attention_mask should put 1 everywhere, so sum over length should be 1 + self.assertEqual( + sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]), + sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]), + ) + + tokens_r_str = tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]) + tokens_p_str = tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]) + + # Rust correctly handles the space before the mask while python doesnt + self.assertSequenceEqual(tokens_p["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]) + self.assertSequenceEqual(tokens_r["input_ids"], [0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]) + + self.assertSequenceEqual( + tokens_p_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] + ) + self.assertSequenceEqual( + tokens_r_str, ["", "A", ",", "", "ĠAllen", "N", "LP", "Ġsentence", ".", ""] + ) + + def test_change_add_prefix_space_and_trim_offsets_args(self): + for trim_offsets, add_prefix_space in itertools.product([True, False], repeat=2): + tokenizer_r = self.rust_tokenizer_class.from_pretrained( + self.tmpdirname, use_fast=True, add_prefix_space=add_prefix_space, trim_offsets=trim_offsets + ) + + pre_tokenizer_state = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()) + post_processor_state = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()) + + self.assertEqual(pre_tokenizer_state["add_prefix_space"], add_prefix_space) + + self.assertEqual(post_processor_state["add_prefix_space"], add_prefix_space) + self.assertEqual(post_processor_state["trim_offsets"], trim_offsets) + + def test_offsets_mapping_with_different_add_prefix_space_and_trim_space_arguments(self): + # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and + # `trim_offsets` + for tokenizer, pretrained_name, kwargs in self.tokenizers_list: + with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): + text_of_1_token = "hello" # `hello` is a token in the vocabulary of `pretrained_name` + text = f"{text_of_1_token} {text_of_1_token}" + + tokenizer_r = self.rust_tokenizer_class.from_pretrained( + pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True + ) + encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) + self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token))) + self.assertEqual( + encoding.offset_mapping[1], + (len(text_of_1_token) + 1, len(text_of_1_token) + 1 + len(text_of_1_token)), + ) + + tokenizer_r = self.rust_tokenizer_class.from_pretrained( + pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=True + ) + encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) + self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token))) + self.assertEqual( + encoding.offset_mapping[1], + (len(text_of_1_token) + 1, len(text_of_1_token) + 1 + len(text_of_1_token)), + ) + + tokenizer_r = self.rust_tokenizer_class.from_pretrained( + pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=False + ) + encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) + self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token))) + self.assertEqual( + encoding.offset_mapping[1], + (len(text_of_1_token), len(text_of_1_token) + 1 + len(text_of_1_token)), + ) + + tokenizer_r = self.rust_tokenizer_class.from_pretrained( + pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=False + ) + encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) + self.assertEqual(encoding.offset_mapping[0], (0, len(text_of_1_token))) + self.assertEqual( + encoding.offset_mapping[1], + (len(text_of_1_token), len(text_of_1_token) + 1 + len(text_of_1_token)), + ) + + text = f" {text}" + + # tokenizer_r = self.rust_tokenizer_class.from_pretrained( + # pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True + # ) + # encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) + # self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token))) + # self.assertEqual( + # encoding.offset_mapping[1], + # (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)), + # ) + + tokenizer_r = self.rust_tokenizer_class.from_pretrained( + pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=True + ) + encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) + self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token))) + self.assertEqual( + encoding.offset_mapping[1], + (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)), + ) + + tokenizer_r = self.rust_tokenizer_class.from_pretrained( + pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=False + ) + encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) + self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(text_of_1_token))) + self.assertEqual( + encoding.offset_mapping[1], + (1 + len(text_of_1_token), 1 + len(text_of_1_token) + 1 + len(text_of_1_token)), + ) + + tokenizer_r = self.rust_tokenizer_class.from_pretrained( + pretrained_name, use_fast=True, add_prefix_space=False, trim_offsets=False + ) + encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False) + self.assertEqual(encoding.offset_mapping[0], (0, 1 + len(text_of_1_token))) + self.assertEqual( + encoding.offset_mapping[1], + (1 + len(text_of_1_token), 1 + len(text_of_1_token) + 1 + len(text_of_1_token)), + )