transformers/tests/tokenization/test_tokenization_fast.py
Arthur 2da8853775
🚨🚨 🚨🚨 [Tokenizer] attemp to fix add_token issues🚨🚨 🚨🚨 (#23909)
* fix test for bart. Order is correct now let's skip BPEs

* ouf

* styling

* fix bert....

* slow refactoring

* current updates

* massive refactoring

* update

* NICE!

* update to see where I am at

* updates

* update

* update

* revert

* updates

* updates

* start supporting legacy_save

* styling

* big update

* revert some changes

* nits

* nniiiiiice

* small fixes

* kinda fix t5 with new behaviour

* major update

* fixup

* fix copies

* today's updates

* fix byt5

* upfate

* update

* update

* updates

* update vocab size test

* Barthez does not use not need the fairseq offset ids

* super calll must be after

* calll super

* move all super init

* move other super init

* fixup

* nits

* more fixes

* nits

* more fixes

* nits

* more fix

* remove useless files

* ouch all of them are affected

* and more!

* small imporvements

* no more sanitize token

* more changes around unique no split tokens

* partially fix more things

* keep legacy save but add warning

* so... more fixes

* updates

* guess deberta tokenizer could be nuked

* fixup

* fixup did some bad things

* nuke it if it breaks

* remove prints and pretrain fast from slow with new format.

* fixups

* Apply suggestions from code review

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fiou

* nit

* by default specials should not be normalized?

* update

* remove brakpoint

* updates

* a lot of updates

* fixup

* fixes revert some changes to match fast

* small nits

* that makes it cleaner

* fix camembert accordingly

* update

* some lest breaking changes

* update

* fixup

* fix byt5 and whisper mostly

* some more fixes, canine's byte vocab

* fix gpt2

* fix most of the perceiver tests (4 left)

* fix layout lmv3

* fixup

* fix copies for gpt2 style

* make sure to only warn once

* fix perciever and gpt2 tests

* some more backward compatibility: also read special tokens map because some ppl use it........////.....

* fixup

* add else when reading

* nits

* fresh updates

* fix copies

* will this make everything faster?

* fixes

* more fixes

* update

* more fixes

* fixup

* is the source of truth right?

* sorry camembert for the troubles

* current updates

* fixup

* update led

* update

* fix regression

* fix single word

* more model specific fixes

* fix t5 tests

* fixup

* more comments

* update

* fix nllb

* rstrip removed

* small fixes

* better handle additional_special_tokens and vocab sizes

* fixing

* styling

* fix 4 / 21

* fixup

* fix nlbb's tests

* some fixes

* fix t5

* fixes

* style

* fix canine tests

* damn this is nice

* nits

* m2m100 nit

* fixups

* fixes!

* fixup

* stash

* fix merge

* revert bad change

* fixup

* correct order for code Llama

* fix speecht5 post merge

* styling

* revert source of 11 fails

* small nits

* all changes in one go

* fnet hack

* fix 2 more tests

* update based on main branch of tokenizers

* fixup

* fix VITS issues

* more fixes

* fix mgp test

* fix camembert issues

* oups camembert still has 2 failing tests

* mluke fixes

* decode fixes

* small nits

* nits

* fix llama and vits

* fix camembert

* smal nits

* more fixes when initialising a fast from a slow and etc

* fix one of the last test

* fix CPM tokenizer test

* fixups

* fix pop2piano

* fixup

* ⚠️ Change tokenizers required version ⚠️

* ⚠️ Change tokenizers required version ⚠️

* "tokenizers>=0.14,<0.15", don't forget smaller than

* fix musicgen tests and pretraiendtokenizerfast

* fix owlvit and all

* update t5

* fix 800 red

* fix tests

* fix the fix of the fix of t5

* styling

* documentation nits

* cache _added_tokens_encoder

* fixups

* Nit

* fix red tests

* one last nit!

* make eveything a lot simpler

* Now it's over 😉

* few small nits

* Apply suggestions from code review

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* updates that work for now

* tests that should no be skipped / changed and fixed next

* fixup

* i am ashamed

* pushe the fix

* update

* fixups

* nits

* fix added_tokens_encoder

* fix canine test

* fix pegasus vocab

* fix transfoXL

* fixup

* whisper needs to be fixed for train new

* pegasus nits

* more pegasus fixes

* minor update

* better error message in failed test

* fix whisper failing test

* fix whisper failing test

* fix pegasus

* fixup

* fix **** pegasus

* reset things

* remove another file

* attempts to fix the strange custome encoder and offset

* nits here and there

* update

* fixup

* nit

* fix the whisper test

* nits nits

* Apply suggestions from code review

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

* updates based on review

* some small update to potentially remove

* nits

* import rlu cache

* Update src/transformers/tokenization_utils_base.py

Co-authored-by: Lysandre Debut <hi@lysand.re>

* move warning to `from_pretrained`

* update tests results now that the special tokens are always added

---------

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
Co-authored-by: Lysandre Debut <hi@lysand.re>
2023-09-18 20:28:36 +02:00

235 lines
12 KiB
Python

# coding=utf-8
# Copyright 2019 HuggingFace Inc.
#
# 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 concurrent.futures
import json
import os
import shutil
import tempfile
import unittest
from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers
from ..test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class PreTrainedTokenizationFastTest(TokenizerTesterMixin, unittest.TestCase):
rust_tokenizer_class = PreTrainedTokenizerFast
test_slow_tokenizer = False
test_rust_tokenizer = True
from_pretrained_vocab_key = "tokenizer_file"
def setUp(self):
self.test_rust_tokenizer = False # because we don't have pretrained_vocab_files_map
super().setUp()
self.test_rust_tokenizer = True
model_paths = ["robot-test/dummy-tokenizer-fast", "robot-test/dummy-tokenizer-wordlevel"]
self.bytelevel_bpe_model_name = "SaulLu/dummy-tokenizer-bytelevel-bpe"
# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
self.tokenizers_list = [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]
tokenizer = PreTrainedTokenizerFast.from_pretrained(model_paths[0])
tokenizer.save_pretrained(self.tmpdirname)
def test_tokenizer_mismatch_warning(self):
# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
# model
pass
@unittest.skip(
"We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any model"
)
def test_encode_decode_with_spaces(self):
pass
def test_pretrained_model_lists(self):
# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
# model
pass
def test_prepare_for_model(self):
# We disable this test for PreTrainedTokenizerFast because it is the only tokenizer that is not linked to any
# model
pass
def test_rust_tokenizer_signature(self):
# PreTrainedTokenizerFast doesn't have tokenizer_file in its signature
pass
def test_training_new_tokenizer(self):
tmpdirname_orig = self.tmpdirname
# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
try:
self.tmpdirname = tempfile.mkdtemp()
tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer.save_pretrained(self.tmpdirname)
super().test_training_new_tokenizer()
finally:
# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
# is restored
shutil.rmtree(self.tmpdirname)
self.tmpdirname = tmpdirname_orig
def test_training_new_tokenizer_with_special_tokens_change(self):
tmpdirname_orig = self.tmpdirname
# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
try:
self.tmpdirname = tempfile.mkdtemp()
tokenizer = self.rust_tokenizer_class.from_pretrained(pretrained_name, **kwargs)
tokenizer.save_pretrained(self.tmpdirname)
super().test_training_new_tokenizer_with_special_tokens_change()
finally:
# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
# is restored
shutil.rmtree(self.tmpdirname)
self.tmpdirname = tmpdirname_orig
def test_training_new_tokenizer_with_bytelevel(self):
tokenizer = self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name)
toy_text_iterator = ("a" for _ in range(1000))
new_tokenizer = tokenizer.train_new_from_iterator(text_iterator=toy_text_iterator, length=1000, vocab_size=50)
encoding_ids = new_tokenizer.encode("a🤗")
self.assertEqual(encoding_ids, [64, 172, 253, 97, 245])
def test_init_from_tokenizers_model(self):
from tokenizers import Tokenizer
sentences = ["Hello, y'all!", "How are you 😁 ? There should not be any issue right?"]
tokenizer = Tokenizer.from_pretrained("t5-base")
# Enable padding
tokenizer.enable_padding(pad_id=0, pad_token="<pad>", length=512, pad_to_multiple_of=8)
self.assertEqual(
tokenizer.padding,
{
"length": 512,
"pad_to_multiple_of": 8,
"pad_id": 0,
"pad_token": "<pad>",
"pad_type_id": 0,
"direction": "right",
},
)
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
tmpdirname = tempfile.mkdtemp()
fast_tokenizer.save_pretrained(tmpdirname)
fast_from_saved = PreTrainedTokenizerFast.from_pretrained(tmpdirname)
for tok in [fast_tokenizer, fast_from_saved]:
self.assertEqual(tok.pad_token_id, 0)
self.assertEqual(tok.padding_side, "right")
self.assertEqual(tok.pad_token, "<pad>")
self.assertEqual(tok.init_kwargs["max_length"], 512)
self.assertEqual(tok.init_kwargs["pad_to_multiple_of"], 8)
# fmt: off
self.assertEqual(tok(sentences, padding = True), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1, 0, 0, 0, 0,0, 0, 0, 0],[ 571, 33, 25, 3, 2, 3, 58, 290, 225, 59, 36, 136, 962, 269, 58, 1]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]})
# fmt: on
tokenizer.enable_truncation(8, stride=0, strategy="longest_first", direction="right")
self.assertEqual(
tokenizer.truncation, {"max_length": 8, "stride": 0, "strategy": "longest_first", "direction": "right"}
)
fast_tokenizer = PreTrainedTokenizerFast(tokenizer_object=tokenizer)
tmpdirname = tempfile.mkdtemp()
fast_tokenizer.save_pretrained(tmpdirname)
fast_from_saved = PreTrainedTokenizerFast.from_pretrained(tmpdirname)
for tok in [fast_tokenizer, fast_from_saved]:
self.assertEqual(tok.truncation_side, "right")
self.assertEqual(tok.init_kwargs["truncation_strategy"], "longest_first")
self.assertEqual(tok.init_kwargs["max_length"], 8)
self.assertEqual(tok.init_kwargs["stride"], 0)
# NOTE even if the model has a default max_length, it is not used...
# thus tok(sentences, truncation = True) does nothing and does not warn either
# fmt: off
self.assertEqual(tok(sentences, truncation = True, max_length = 8), {'input_ids': [[8774, 6, 3, 63, 31, 1748, 55, 1],[ 571, 33, 25, 3, 2, 3, 58, 1]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0],[0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1]]})
# fmt: on
@require_tokenizers
class TokenizerVersioningTest(unittest.TestCase):
def test_local_versioning(self):
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
json_tokenizer = json.loads(tokenizer._tokenizer.to_str())
json_tokenizer["model"]["vocab"]["huggingface"] = len(tokenizer)
with tempfile.TemporaryDirectory() as tmp_dir:
# Hack to save this in the tokenizer_config.json
tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.4.0.0.json"]
tokenizer.save_pretrained(tmp_dir)
json.dump(json_tokenizer, open(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), "w"))
# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)
self.assertEqual(len(new_tokenizer), len(tokenizer) + 1)
json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str())
self.assertIn("huggingface", json_tokenizer["model"]["vocab"])
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
shutil.move(os.path.join(tmp_dir, "tokenizer.4.0.0.json"), os.path.join(tmp_dir, "tokenizer.42.0.0.json"))
tokenizer.init_kwargs["fast_tokenizer_files"] = ["tokenizer.42.0.0.json"]
tokenizer.save_pretrained(tmp_dir)
new_tokenizer = AutoTokenizer.from_pretrained(tmp_dir)
self.assertEqual(len(new_tokenizer), len(tokenizer))
json_tokenizer = json.loads(new_tokenizer._tokenizer.to_str())
self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"])
def test_repo_versioning(self):
# This repo has two tokenizer files, one for v4.0.0 and above with an added token, one for versions lower.
repo = "hf-internal-testing/test-two-tokenizers"
# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
tokenizer = AutoTokenizer.from_pretrained(repo)
self.assertEqual(len(tokenizer), 28997)
json_tokenizer = json.loads(tokenizer._tokenizer.to_str())
self.assertIn("huggingface", json_tokenizer["model"]["vocab"])
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
old_transformers.tokenization_utils_base.__version__ = "3.0.0"
old_tokenizer = old_transformers.models.auto.AutoTokenizer.from_pretrained(repo)
self.assertEqual(len(old_tokenizer), 28996)
json_tokenizer = json.loads(old_tokenizer._tokenizer.to_str())
self.assertNotIn("huggingface", json_tokenizer["model"]["vocab"])
@require_tokenizers
class ReduceMutableBorrowTests(unittest.TestCase):
def test_async_share_tokenizer(self):
# See https://github.com/huggingface/transformers/pull/12550
# and https://github.com/huggingface/tokenizers/issues/537
tokenizer = PreTrainedTokenizerFast.from_pretrained("robot-test/dummy-tokenizer-wordlevel")
text = "The Matrix is a 1999 science fiction action film."
with concurrent.futures.ThreadPoolExecutor() as executor:
futures = [executor.submit(self.fetch, tokenizer, text) for i in range(10)]
return_value = [future.result() for future in futures]
self.assertEqual(return_value, [[1, 10, 0, 8, 0, 18, 0, 0, 0, 2] for i in range(10)])
def fetch(self, tokenizer, text):
return tokenizer.encode(text, truncation="longest_first", padding="longest")