Add missing tokenizer tests - Longformer

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",
+            "<unk>",
+        ]
+        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": "<unk>"}
+
+        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": "<s>"})
+        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 = "<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 <mask> sequence"
+        sequence_nospace = "Encode <mask>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, <mask> 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, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
+                )
+                self.assertSequenceEqual(
+                    tokens_r_str, ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]
+                )
+
+    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)),
+                )