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transformers/tests/models/gpt2/test_tokenization_gpt2.py

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# coding=utf-8
# 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 transformers import AutoTokenizer, GPT2Tokenizer, GPT2TokenizerFast
from transformers.models.gpt2.tokenization_gpt2 import VOCAB_FILES_NAMES
from transformers.testing_utils import require_jinja, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@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
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>",
"<|endoftext|>",
]
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 GPT2Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
def get_rust_tokenizer(self, **kwargs):
kwargs.update(self.special_tokens_map)
return GPT2TokenizerFast.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 = 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:
return
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)
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.rust_tokenizer_class.from_pretrained(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="<pad>")
# 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))
# 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)
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_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("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])
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