# Copyright 2020 The HuggingFace Team. 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. import json import os import unittest from functools import lru_cache from transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast from transformers.models.gpt2.tokenization_gpt2 import VOCAB_FILES_NAMES from transformers.testing_utils import require_jinja, require_tiktoken, require_tokenizers from ...test_tokenization_common import TokenizerTesterMixin, use_cache_if_possible @require_tokenizers class GPT2TokenizationTest(TokenizerTesterMixin, unittest.TestCase): from_pretrained_id = "openai-community/gpt2" tokenizer_class = GPT2Tokenizer rust_tokenizer_class = GPT2TokenizerFast test_rust_tokenizer = True from_pretrained_kwargs = {"add_prefix_space": True} test_seq2seq = False @classmethod def setUpClass(cls): super().setUpClass() # 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", "", "<|endoftext|>", ] vocab_tokens = dict(zip(vocab, range(len(vocab)))) merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""] cls.special_tokens_map = {"unk_token": ""} cls.vocab_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["vocab_file"]) cls.merges_file = os.path.join(cls.tmpdirname, VOCAB_FILES_NAMES["merges_file"]) with open(cls.vocab_file, "w", encoding="utf-8") as fp: fp.write(json.dumps(vocab_tokens) + "\n") with open(cls.merges_file, "w", encoding="utf-8") as fp: fp.write("\n".join(merges)) @classmethod @use_cache_if_possible @lru_cache(maxsize=64) def get_tokenizer(cls, pretrained_name=None, **kwargs): kwargs.update(cls.special_tokens_map) pretrained_name = pretrained_name or cls.tmpdirname return GPT2Tokenizer.from_pretrained(pretrained_name, **kwargs) @classmethod @use_cache_if_possible @lru_cache(maxsize=64) def get_rust_tokenizer(cls, pretrained_name=None, **kwargs): kwargs.update(cls.special_tokens_map) pretrained_name = pretrained_name or cls.tmpdirname return GPT2TokenizerFast.from_pretrained(pretrained_name, **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 = GPT2Tokenizer(self.vocab_file, self.merges_file, **self.special_tokens_map) text = "lower newer" bpe_tokens = ["\u0120low", "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 = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) def test_rust_and_python_full_tokenizers(self): if not self.test_rust_tokenizer: self.skipTest(reason="test_rust_tokenizer is set to False") tokenizer = self.get_tokenizer() rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True) sequence = "lower newer" # Testing tokenization tokens = tokenizer.tokenize(sequence, add_prefix_space=True) rust_tokens = rust_tokenizer.tokenize(sequence) self.assertListEqual(tokens, rust_tokens) # Testing conversion to ids without special tokens ids = tokenizer.encode(sequence, add_special_tokens=False, add_prefix_space=True) rust_ids = rust_tokenizer.encode(sequence, add_special_tokens=False) self.assertListEqual(ids, rust_ids) # Testing conversion to ids with special tokens rust_tokenizer = self.get_rust_tokenizer(add_prefix_space=True) ids = tokenizer.encode(sequence, add_prefix_space=True) rust_ids = rust_tokenizer.encode(sequence) self.assertListEqual(ids, rust_ids) # Testing the unknown token input_tokens = tokens + [rust_tokenizer.unk_token] input_bpe_tokens = [14, 15, 10, 9, 3, 2, 15, 19] self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(input_tokens), input_bpe_tokens) @unittest.skip def test_pretokenized_inputs(self, *args, **kwargs): # It's very difficult to mix/test pretokenization with byte-level # And get both GPT2 and Roberta to work at the same time (mostly an issue of adding a space before the string) pass def test_padding(self, max_length=15): for tokenizer, pretrained_name, kwargs in self.tokenizers_list: with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"): tokenizer_r = self.get_rust_tokenizer(pretrained_name, **kwargs) # Simple input s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] p = ("This is a simple input", "This is a pair") p2 = [ ("This is a simple input 1", "This is a simple input 2"), ("This is a simple pair 1", "This is a simple pair 2"), ] # Simple input tests self.assertRaises(ValueError, tokenizer_r.encode, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises(ValueError, tokenizer_r.encode_plus, s, max_length=max_length, padding="max_length") # Simple input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, s2, max_length=max_length, padding="max_length", ) # Pair input self.assertRaises(ValueError, tokenizer_r.encode, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises(ValueError, tokenizer_r.encode_plus, p, max_length=max_length, padding="max_length") # Pair input self.assertRaises( ValueError, tokenizer_r.batch_encode_plus, p2, max_length=max_length, padding="max_length", ) def test_padding_if_pad_token_set_slow(self): tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, pad_token="") # Simple input s = "This is a simple input" s2 = ["This is a simple input looooooooong", "This is a simple input"] p = ("This is a simple input", "This is a pair") p2 = [ ("This is a simple input loooooong", "This is a simple input"), ("This is a simple pair loooooong", "This is a simple pair"), ] pad_token_id = tokenizer.pad_token_id out_s = tokenizer(s, padding="max_length", max_length=30, return_tensors="np") out_s2 = tokenizer(s2, padding=True, truncate=True, return_tensors="np") out_p = tokenizer(*p, padding="max_length", max_length=60, return_tensors="np") out_p2 = tokenizer(p2, padding=True, truncate=True, return_tensors="np") # s # test single string max_length padding self.assertEqual(out_s["input_ids"].shape[-1], 30) self.assertTrue(pad_token_id in out_s["input_ids"]) self.assertTrue(0 in out_s["attention_mask"]) # s2 # test automatic padding self.assertEqual(out_s2["input_ids"].shape[-1], 33) # long slice doesn't have padding self.assertFalse(pad_token_id in out_s2["input_ids"][0]) self.assertFalse(0 in out_s2["attention_mask"][0]) # short slice does have padding self.assertTrue(pad_token_id in out_s2["input_ids"][1]) self.assertTrue(0 in out_s2["attention_mask"][1]) # p # test single pair max_length padding self.assertEqual(out_p["input_ids"].shape[-1], 60) self.assertTrue(pad_token_id in out_p["input_ids"]) self.assertTrue(0 in out_p["attention_mask"]) # p2 # test automatic padding pair self.assertEqual(out_p2["input_ids"].shape[-1], 52) # long slice pair doesn't have padding self.assertFalse(pad_token_id in out_p2["input_ids"][0]) self.assertFalse(0 in out_p2["attention_mask"][0]) # short slice pair does have padding self.assertTrue(pad_token_id in out_p2["input_ids"][1]) self.assertTrue(0 in out_p2["attention_mask"][1]) def test_add_bos_token_slow(self): bos_token = "$$$" tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname, bos_token=bos_token, add_bos_token=True) s = "This is a simple input" s2 = ["This is a simple input 1", "This is a simple input 2"] bos_token_id = tokenizer.bos_token_id out_s = tokenizer(s) out_s2 = tokenizer(s2) self.assertEqual(out_s.input_ids[0], bos_token_id) self.assertTrue(all(o[0] == bos_token_id for o in out_s2.input_ids)) decode_s = tokenizer.decode(out_s.input_ids) decode_s2 = tokenizer.batch_decode(out_s2.input_ids) self.assertTrue(decode_s.startswith(bos_token)) self.assertTrue(all(d.startswith(bos_token) for d in decode_s2)) @unittest.skip(reason="tokenizer has no padding token") def test_padding_different_model_input_name(self): pass def test_special_tokens_mask_input_pairs_and_bos_token(self): # TODO: change to self.get_tokenizers() when the fast version is implemented tokenizers = [self.get_tokenizer(do_lower_case=False, add_bos_token=True)] for tokenizer in tokenizers: with self.subTest(f"{tokenizer.__class__.__name__}"): sequence_0 = "Encode this." sequence_1 = "This one too please." encoded_sequence = tokenizer.encode(sequence_0, add_special_tokens=False) encoded_sequence += tokenizer.encode(sequence_1, add_special_tokens=False) encoded_sequence_dict = tokenizer.encode_plus( sequence_0, sequence_1, add_special_tokens=True, return_special_tokens_mask=True, ) encoded_sequence_w_special = encoded_sequence_dict["input_ids"] special_tokens_mask = encoded_sequence_dict["special_tokens_mask"] self.assertEqual(len(special_tokens_mask), len(encoded_sequence_w_special)) filtered_sequence = [ (x if not special_tokens_mask[i] else None) for i, x in enumerate(encoded_sequence_w_special) ] filtered_sequence = [x for x in filtered_sequence if x is not None] self.assertEqual(encoded_sequence, filtered_sequence) @require_jinja def test_tokenization_for_chat(self): tokenizer = GPT2Tokenizer.from_pretrained(self.tmpdirname) tokenizer.chat_template = "{% for message in messages %}{{ message.content }}{{ eos_token }}{% endfor %}" test_chats = [ [{"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}], [ {"role": "system", "content": "You are a helpful chatbot."}, {"role": "user", "content": "Hello!"}, {"role": "assistant", "content": "Nice to meet you."}, ], [{"role": "assistant", "content": "Nice to meet you."}, {"role": "user", "content": "Hello!"}], ] tokenized_chats = [tokenizer.apply_chat_template(test_chat) for test_chat in test_chats] # fmt: off expected_tokens = [[20, 1, 20, 10, 20, 4, 3, 10, 20, 10, 20, 3, 0, 20, 20, 20, 0, 10, 20, 20, 20, 6, 20, 1, 6, 20, 20, 20, 3, 0, 0, 1, 20, 20], [20, 1, 20, 10, 20, 4, 3, 10, 20, 10, 20, 3, 0, 20, 20, 20, 0, 10, 20, 20, 20, 6, 20, 1, 6, 20, 20, 20, 3, 0, 0, 1, 20, 20, 20, 7, 20, 3, 10, 6, 1, 10, 20, 3, 3, 6, 10, 20, 1, 20, 20, 20], [20, 7, 20, 3, 10, 6, 1, 10, 20, 3, 3, 6, 10, 20, 1, 20, 20, 20, 20, 3, 0, 0, 1, 20, 20]] # fmt: on for tokenized_chat, expected_tokens in zip(tokenized_chats, expected_tokens): self.assertListEqual(tokenized_chat, expected_tokens) @require_tiktoken def test_tokenization_tiktoken(self): from tiktoken import encoding_name_for_model from transformers.integrations.tiktoken import convert_tiktoken_to_fast encoding = encoding_name_for_model("gpt2") convert_tiktoken_to_fast(encoding, self.tmpdirname) tiktoken_fast_tokenizer = GPT2TokenizerFast.from_pretrained(self.tmpdirname) rust_tokenizer = GPT2TokenizerFast.from_pretrained("openai-community/gpt2") sequence = "lower newer" self.assertEqual( rust_tokenizer.decode(rust_tokenizer.encode(sequence)), tiktoken_fast_tokenizer.decode(rust_tokenizer.encode(sequence)), ) @require_tokenizers class OPTTokenizationTest(unittest.TestCase): def test_serialize_deserialize_fast_opt(self): # More context: # https://huggingface.co/wjmcat/opt-350m-paddle/discussions/1 # https://huggingface.slack.com/archives/C01N44FJDHT/p1653511495183519 # https://github.com/huggingface/transformers/pull/17088#discussion_r871246439 tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", from_slow=True) text = "A photo of a cat" tokens_ids = tokenizer.encode( text, ) self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758]) tokenizer.save_pretrained("test_opt") tokenizer = AutoTokenizer.from_pretrained("./test_opt") tokens_ids = tokenizer.encode( text, ) self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758]) def test_fast_slow_equivalence(self): tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", use_slow=True) text = "A photo of a cat" tokens_ids = tokenizer.encode( text, ) # Same as above self.assertEqual(tokens_ids, [2, 250, 1345, 9, 10, 4758]) @unittest.skip(reason="This test is failing because of a bug in the fast tokenizer") def test_users_can_modify_bos(self): tokenizer = AutoTokenizer.from_pretrained("facebook/opt-350m", from_slow=True) tokenizer.bos_token = "bos" tokenizer.bos_token_id = tokenizer.get_vocab()["bos"] text = "A photo of a cat" tokens_ids = tokenizer.encode( text, ) # We changed the bos token self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758]) tokenizer.save_pretrained("./tok") tokenizer = AutoTokenizer.from_pretrained("./tok") self.assertTrue(tokenizer.is_fast) tokens_ids = tokenizer.encode( text, ) self.assertEqual(tokens_ids, [31957, 250, 1345, 9, 10, 4758])