Add m2m100 (#10236)
* m2m_100 * no layernorm_embedding * sinusoidal positional embeddings * update pos embeddings * add default config values * tokenizer * add conversion script * fix config * fix pos embed * remove _float_tensor * update tokenizer * update lang codes * handle lang codes * fix pos embeds * fix spm key * put embedding weights on device * remove qa and seq classification heads * fix convert script * lang codes pn one line * fix embeds * fix tokenizer * fix tokenizer * add fast tokenizer * style * M2M100MT => M2M100 * fix copyright, style * tokenizer converter * vocab file * remove fast tokenizer * fix embeds * fix tokenizer * fix tests * add tokenizer tests * add integration test * quality * fix model name * fix test * doc * doc * fix doc * add copied from statements * fix tokenizer tests * apply review suggestions * fix urls * fix shift_tokens_right * apply review suggestions * fix * fix doc * add lang code to id * remove unused function * update checkpoint names * fix copy * fix tokenizer * fix checkpoint names * fix merge issue * style
This commit is contained in:
Suraj Patil
193
tests/test_tokenization_m2m_100.py
Normal file
193
tests/test_tokenization_m2m_100.py
Normal file
@@ -0,0 +1,193 @@
|
||||
# Copyright 2021 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 os
|
||||
import tempfile
|
||||
import unittest
|
||||
from pathlib import Path
|
||||
from shutil import copyfile
|
||||
|
||||
from transformers import M2M100Tokenizer, is_torch_available
|
||||
from transformers.file_utils import is_sentencepiece_available
|
||||
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch
|
||||
|
||||
|
||||
if is_sentencepiece_available():
|
||||
from transformers.models.m2m_100.tokenization_m2m_100 import save_json, VOCAB_FILES_NAMES
|
||||
|
||||
from .test_tokenization_common import TokenizerTesterMixin
|
||||
|
||||
|
||||
if is_sentencepiece_available():
|
||||
SAMPLE_SP = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures/test_sentencepiece.model")
|
||||
|
||||
|
||||
if is_torch_available():
|
||||
from transformers.models.m2m_100.modeling_m2m_100 import shift_tokens_right
|
||||
|
||||
EN_CODE = 128022
|
||||
FR_CODE = 128028
|
||||
|
||||
|
||||
@require_sentencepiece
|
||||
class M2M100TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
|
||||
tokenizer_class = M2M100Tokenizer
|
||||
test_rust_tokenizer = False
|
||||
test_seq2seq = False
|
||||
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
|
||||
vocab = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
|
||||
vocab_tokens = dict(zip(vocab, range(len(vocab))))
|
||||
save_dir = Path(self.tmpdirname)
|
||||
save_json(vocab_tokens, save_dir / VOCAB_FILES_NAMES["vocab_file"])
|
||||
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
|
||||
copyfile(SAMPLE_SP, save_dir / VOCAB_FILES_NAMES["spm_file"])
|
||||
|
||||
tokenizer = M2M100Tokenizer.from_pretrained(self.tmpdirname)
|
||||
tokenizer.save_pretrained(self.tmpdirname)
|
||||
|
||||
def get_tokenizer(self, **kwargs):
|
||||
return M2M100Tokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def get_input_output_texts(self, tokenizer):
|
||||
return (
|
||||
"This is a test",
|
||||
"This is a test",
|
||||
)
|
||||
|
||||
@unittest.skip("Skip this test while all models are still to be uploaded.")
|
||||
def test_pretrained_model_lists(self):
|
||||
pass
|
||||
|
||||
def test_full_tokenizer(self):
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
tokens = tokenizer.tokenize("This is a test")
|
||||
self.assertListEqual(tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
|
||||
|
||||
self.assertListEqual(
|
||||
tokenizer.convert_tokens_to_ids(tokens),
|
||||
[2, 3, 4, 5, 6],
|
||||
)
|
||||
|
||||
back_tokens = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6])
|
||||
self.assertListEqual(back_tokens, ["▁This", "▁is", "▁a", "▁t", "est"])
|
||||
|
||||
text = tokenizer.convert_tokens_to_string(tokens)
|
||||
self.assertEqual(text, "This is a test")
|
||||
|
||||
|
||||
@require_torch
|
||||
@require_sentencepiece
|
||||
@require_tokenizers
|
||||
class M2M100TokenizerIntegrationTest(unittest.TestCase):
|
||||
checkpoint_name = "facebook/m2m100_418M"
|
||||
src_text = [
|
||||
"In my opinion, there are two levels of response from the French government.",
|
||||
"NSA Affair Emphasizes Complete Lack of Debate on Intelligence",
|
||||
]
|
||||
tgt_text = [
|
||||
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
|
||||
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
|
||||
]
|
||||
|
||||
# fmt: off
|
||||
expected_src_tokens = [EN_CODE, 593, 1949, 115781, 4, 71586, 4234, 60633, 126233, 432, 123808, 15592, 1197, 117132, 120618, 5, 2]
|
||||
# fmt: on
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
cls.tokenizer: M2M100Tokenizer = M2M100Tokenizer.from_pretrained(
|
||||
cls.checkpoint_name, src_lang="en", tgt_lang="fr"
|
||||
)
|
||||
cls.pad_token_id = 1
|
||||
return cls
|
||||
|
||||
def check_language_codes(self):
|
||||
self.assertEqual(self.tokenizer.get_lang_id("ar"), 128006)
|
||||
self.assertEqual(self.tokenizer.get_lang_id("en"), 128022)
|
||||
self.assertEqual(self.tokenizer.get_lang_id("ro"), 128076)
|
||||
self.assertEqual(self.tokenizer.get_lang_id("mr"), 128063)
|
||||
|
||||
def test_tokenizer_batch_encode_plus(self):
|
||||
self.tokenizer.src_lang = "en"
|
||||
ids = self.tokenizer.batch_encode_plus(self.src_text).input_ids[0]
|
||||
self.assertListEqual(self.expected_src_tokens, ids)
|
||||
|
||||
def test_tokenizer_decode_ignores_language_codes(self):
|
||||
self.assertIn(FR_CODE, self.tokenizer.all_special_ids)
|
||||
# fmt: off
|
||||
generated_ids = [FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
|
||||
# fmt: on
|
||||
result = self.tokenizer.decode(generated_ids, skip_special_tokens=True)
|
||||
expected_french = self.tokenizer.decode(generated_ids[1:], skip_special_tokens=True)
|
||||
self.assertEqual(result, expected_french)
|
||||
self.assertNotIn(self.tokenizer.eos_token, result)
|
||||
|
||||
def test_special_tokens_unaffacted_by_save_load(self):
|
||||
tmpdirname = tempfile.mkdtemp()
|
||||
original_special_tokens = self.tokenizer.lang_token_to_id
|
||||
self.tokenizer.save_pretrained(tmpdirname)
|
||||
new_tok = M2M100Tokenizer.from_pretrained(tmpdirname)
|
||||
self.assertDictEqual(new_tok.lang_token_to_id, original_special_tokens)
|
||||
|
||||
@require_torch
|
||||
def test_batch_fairseq_parity(self):
|
||||
self.tokenizer.src_lang = "en"
|
||||
self.tokenizer.tgt_lang = "fr"
|
||||
|
||||
batch = self.tokenizer(self.src_text, padding=True, return_tensors="pt")
|
||||
with self.tokenizer.as_target_tokenizer():
|
||||
batch["labels"] = self.tokenizer(self.tgt_text, padding=True, return_tensors="pt").input_ids
|
||||
|
||||
batch["decoder_input_ids"] = shift_tokens_right(
|
||||
batch["labels"], self.tokenizer.pad_token_id, self.tokenizer.eos_token_id
|
||||
)
|
||||
|
||||
for k in batch:
|
||||
batch[k] = batch[k].tolist()
|
||||
# batch = {k: v.tolist() for k,v in batch.items()}
|
||||
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
|
||||
# batch.decoder_inputs_ids[0][0] ==
|
||||
assert batch.input_ids[1][0] == EN_CODE
|
||||
assert batch.input_ids[1][-1] == 2
|
||||
assert batch.labels[1][0] == FR_CODE
|
||||
assert batch.labels[1][-1] == 2
|
||||
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
|
||||
|
||||
@require_torch
|
||||
def test_src_lang_setter(self):
|
||||
self.tokenizer.src_lang = "mr"
|
||||
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("mr")])
|
||||
self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])
|
||||
|
||||
self.tokenizer.src_lang = "zh"
|
||||
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("zh")])
|
||||
self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])
|
||||
|
||||
@require_torch
|
||||
def test_as_target_tokenizer(self):
|
||||
self.tokenizer.tgt_lang = "mr"
|
||||
with self.tokenizer.as_target_tokenizer():
|
||||
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("mr")])
|
||||
self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])
|
||||
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])
|
||||
|
||||
self.tokenizer.tgt_lang = "zh"
|
||||
with self.tokenizer.as_target_tokenizer():
|
||||
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id("zh")])
|
||||
self.assertListEqual(self.tokenizer.suffix_tokens, [self.tokenizer.eos_token_id])
|
||||
self.assertListEqual(self.tokenizer.prefix_tokens, [self.tokenizer.get_lang_id(self.tokenizer.src_lang)])
|
||||
Reference in New Issue
Block a user