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Move test model folders (#17034)

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* move test model folders (TODO: fix imports and others)

* fix (potentially partially) imports (in model test modules)

* fix (potentially partially) imports (in tokenization test modules)

* fix (potentially partially) imports (in feature extraction test modules)

* fix import utils.test_modeling_tf_core

* fix path ../fixtures/

* fix imports about generation.test_generation_flax_utils

* fix more imports

* fix fixture path

* fix get_test_dir

* update module_to_test_file

* fix get_tests_dir from wrong transformers.utils

* update config.yml (CircleCI)

* fix style

* remove missing imports

* update new model script

* update check_repo

* update SPECIAL_MODULE_TO_TEST_MAP

* fix style

* add __init__

* update self-scheduled

* fix add_new_model scripts

* check one way to get location back

* python setup.py build install

* fix import in test auto

* update self-scheduled.yml

* update slack notification script

* Add comments about artifact names

* fix for yolos

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2022-05-03 14:42:02 +02:00
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parent cd9274d010
commit 19420fd99e
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tests/models/m2m_100/__init__.py Normal file
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tests/models/m2m_100/test_modeling_m2m_100.py Normal file
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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. 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.
""" Testing suite for the PyTorch M2M100 model. """
import copy
import tempfile
import unittest
from transformers import M2M100Config, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_generation_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
if is_torch_available():
import torch
from transformers import M2M100ForConditionalGeneration, M2M100Model, M2M100Tokenizer
from transformers.models.m2m_100.modeling_m2m_100 import M2M100Decoder, M2M100Encoder
def prepare_m2m_100_inputs_dict(
config,
input_ids,
decoder_input_ids,
attention_mask=None,
decoder_attention_mask=None,
head_mask=None,
decoder_head_mask=None,
cross_attn_head_mask=None,
):
if attention_mask is None:
attention_mask = input_ids.ne(config.pad_token_id)
if decoder_attention_mask is None:
decoder_attention_mask = decoder_input_ids.ne(config.pad_token_id)
if head_mask is None:
head_mask = torch.ones(config.encoder_layers, config.encoder_attention_heads, device=torch_device)
if decoder_head_mask is None:
decoder_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
if cross_attn_head_mask is None:
cross_attn_head_mask = torch.ones(config.decoder_layers, config.decoder_attention_heads, device=torch_device)
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class M2M100ModelTester:
def __init__(
self,
parent,
batch_size=13,
seq_length=7,
is_training=True,
use_labels=False,
vocab_size=99,
hidden_size=16,
num_hidden_layers=2,
num_attention_heads=4,
intermediate_size=4,
hidden_act="relu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
max_position_embeddings=20,
eos_token_id=2,
pad_token_id=1,
bos_token_id=0,
):
self.parent = parent
self.batch_size = batch_size
self.seq_length = seq_length
self.is_training = is_training
self.use_labels = use_labels
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.encoder_layerdrop = encoder_layerdrop
self.decoder_layerdrop = decoder_layerdrop
self.max_position_embeddings = max_position_embeddings
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.bos_token_id = bos_token_id
def prepare_config_and_inputs(self):
input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
input_ids[:, -1] = self.eos_token_id # Eos Token
decoder_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
input_ids = input_ids.clamp(self.pad_token_id + 1)
decoder_input_ids = decoder_input_ids.clamp(self.pad_token_id + 1)
config = self.get_config()
inputs_dict = prepare_m2m_100_inputs_dict(config, input_ids, decoder_input_ids)
return config, inputs_dict
def get_config(self):
return M2M100Config(
vocab_size=self.vocab_size,
d_model=self.hidden_size,
encoder_layers=self.num_hidden_layers,
decoder_layers=self.num_hidden_layers,
encoder_attention_heads=self.num_attention_heads,
decoder_attention_heads=self.num_attention_heads,
encoder_ffn_dim=self.intermediate_size,
decoder_ffn_dim=self.intermediate_size,
dropout=self.hidden_dropout_prob,
attention_dropout=self.attention_probs_dropout_prob,
encoder_layerdrop=self.encoder_layerdrop,
decoder_layerdrop=self.decoder_layerdrop,
max_position_embeddings=self.max_position_embeddings,
eos_token_id=self.eos_token_id,
bos_token_id=self.bos_token_id,
pad_token_id=self.pad_token_id,
)
def prepare_config_and_inputs_for_common(self):
config, inputs_dict = self.prepare_config_and_inputs()
return config, inputs_dict
def create_and_check_decoder_model_past_large_inputs(self, config, inputs_dict):
model = M2M100Model(config=config).get_decoder().to(torch_device).eval()
input_ids = inputs_dict["input_ids"]
attention_mask = inputs_dict["attention_mask"]
head_mask = inputs_dict["head_mask"]
# first forward pass
outputs = model(input_ids, attention_mask=attention_mask, head_mask=head_mask, use_cache=True)
output, past_key_values = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
next_tokens = ids_tensor((self.batch_size, 3), config.vocab_size)
next_attn_mask = ids_tensor((self.batch_size, 3), 2)
# append to next input_ids and
next_input_ids = torch.cat([input_ids, next_tokens], dim=-1)
next_attention_mask = torch.cat([attention_mask, next_attn_mask], dim=-1)
output_from_no_past = model(next_input_ids, attention_mask=next_attention_mask)["last_hidden_state"]
output_from_past = model(next_tokens, attention_mask=next_attention_mask, past_key_values=past_key_values)[
"last_hidden_state"
]
# select random slice
random_slice_idx = ids_tensor((1,), output_from_past.shape[-1]).item()
output_from_no_past_slice = output_from_no_past[:, -3:, random_slice_idx].detach()
output_from_past_slice = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(output_from_past_slice, output_from_no_past_slice, atol=1e-2))
def check_encoder_decoder_model_standalone(self, config, inputs_dict):
model = M2M100Model(config=config).to(torch_device).eval()
outputs = model(**inputs_dict)
encoder_last_hidden_state = outputs.encoder_last_hidden_state
last_hidden_state = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
encoder = model.get_encoder()
encoder.save_pretrained(tmpdirname)
encoder = M2M100Encoder.from_pretrained(tmpdirname).to(torch_device)
encoder_last_hidden_state_2 = encoder(inputs_dict["input_ids"], attention_mask=inputs_dict["attention_mask"])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_2 - encoder_last_hidden_state).abs().max().item() < 1e-3)
with tempfile.TemporaryDirectory() as tmpdirname:
decoder = model.get_decoder()
decoder.save_pretrained(tmpdirname)
decoder = M2M100Decoder.from_pretrained(tmpdirname).to(torch_device)
last_hidden_state_2 = decoder(
input_ids=inputs_dict["decoder_input_ids"],
attention_mask=inputs_dict["decoder_attention_mask"],
encoder_hidden_states=encoder_last_hidden_state,
encoder_attention_mask=inputs_dict["attention_mask"],
)[0]
self.parent.assertTrue((last_hidden_state_2 - last_hidden_state).abs().max().item() < 1e-3)
@require_torch
class M2M100ModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
all_model_classes = (
(
M2M100Model,
M2M100ForConditionalGeneration,
)
if is_torch_available()
else ()
)
all_generative_model_classes = (M2M100ForConditionalGeneration,) if is_torch_available() else ()
is_encoder_decoder = True
test_pruning = False
test_missing_keys = False
def setUp(self):
self.model_tester = M2M100ModelTester(self)
self.config_tester = ConfigTester(self, config_class=M2M100Config)
def test_config(self):
self.config_tester.run_common_tests()
def test_save_load_strict(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
model = model_class(config)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
model2, info = model_class.from_pretrained(tmpdirname, output_loading_info=True)
self.assertEqual(info["missing_keys"], [])
def test_decoder_model_past_with_large_inputs(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*config_and_inputs)
def test_encoder_decoder_model_standalone(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*config_and_inputs)
def test_inputs_embeds(self):
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (M2M100Model, M2M100ForConditionalGeneration):
model = model_class(config)
model.to(torch_device)
model.eval()
inputs = copy.deepcopy(self._prepare_for_class(inputs_dict, model_class))
if not self.is_encoder_decoder:
input_ids = inputs["input_ids"]
del inputs["input_ids"]
else:
encoder_input_ids = inputs["input_ids"]
decoder_input_ids = inputs.get("decoder_input_ids", encoder_input_ids)
del inputs["input_ids"]
inputs.pop("decoder_input_ids", None)
wte = model.get_input_embeddings()
if not self.is_encoder_decoder:
inputs["inputs_embeds"] = wte(input_ids)
else:
inputs["inputs_embeds"] = wte(encoder_input_ids)
inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
with torch.no_grad():
model(**inputs)[0]
def test_generate_fp16(self):
config, input_dict = self.model_tester.prepare_config_and_inputs()
input_ids = input_dict["input_ids"]
attention_mask = input_ids.ne(1).to(torch_device)
model = M2M100ForConditionalGeneration(config).eval().to(torch_device)
if torch_device == "cuda":
model.half()
model.generate(input_ids, attention_mask=attention_mask)
model.generate(num_beams=4, do_sample=True, early_stopping=False, num_return_sequences=3)
def _long_tensor(tok_lst):
return torch.tensor(tok_lst, dtype=torch.long, device=torch_device)
TOLERANCE = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class M2M100ModelIntegrationTests(unittest.TestCase):
@cached_property
def default_tokenizer(self):
return M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
def test_inference_no_head(self):
model = M2M100Model.from_pretrained("facebook/m2m100_418M").to(torch_device)
input_ids = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]])
decoder_input_ids = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]])
inputs_dict = prepare_m2m_100_inputs_dict(model.config, input_ids, decoder_input_ids)
with torch.no_grad():
output = model(**inputs_dict)[0]
expected_shape = torch.Size((1, 11, 1024))
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
def test_inference_head(self):
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M").to(torch_device)
# change to intended input
input_ids = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]])
decoder_input_ids = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]])
inputs_dict = prepare_m2m_100_inputs_dict(model.config, input_ids, decoder_input_ids)
with torch.no_grad():
output = model(**inputs_dict)[0]
expected_shape = torch.Size((1, 11, model.config.vocab_size))
self.assertEqual(output.shape, expected_shape)
# change to expected output here
expected_slice = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]], device=torch_device
)
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=TOLERANCE))
def test_seq_to_seq_generation(self):
model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M").to(torch_device)
tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M", src_lang="fr", tgt_lang="en")
src_fr = [
"L'affaire NSA souligne l'absence totale de débat sur le renseignement",
"Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.",
"Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de l'ampleur de la surveillance américaine sur l'ensemble des communications en France.",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
dct = tokenizer(src_fr, padding=True, return_tensors="pt")
hypotheses_batch = model.generate(
input_ids=dct["input_ids"].to(torch_device),
attention_mask=dct["attention_mask"].to(torch_device),
num_beams=5,
forced_bos_token_id=tokenizer.get_lang_id("en"),
)
expected_en = [
"The NSA case highlights the total absence of intelligence debate",
"I think there are two levels of response from the French government.",
"When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S. Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all communications in France.",
]
generated = tokenizer.batch_decode(
hypotheses_batch.tolist(), clean_up_tokenization_spaces=True, skip_special_tokens=True
)
assert generated == expected_en

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tests/models/m2m_100/test_tokenization_m2m_100.py Normal file
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# 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 tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import M2M100Tokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
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 = get_tests_dir("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
test_sentencepiece = True
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",
)
def test_convert_token_and_id(self):
"""Test ``_convert_token_to_id`` and ``_convert_id_to_token``."""
token = "</s>"
token_id = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(token), token_id)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(token_id), token)
def test_get_vocab(self):
vocab_keys = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "</s>")
self.assertEqual(vocab_keys[1], "<unk>")
self.assertEqual(vocab_keys[-1], "<s>")
self.assertEqual(len(vocab_keys), 10)
def test_vocab_size(self):
self.assertEqual(self.get_tokenizer().vocab_size, 117)
@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")
@slow
def test_tokenizer_integration(self):
# fmt: off
expected_encoding = {'input_ids': [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 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]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=expected_encoding,
model_name="facebook/m2m100_418M",
revision="c168bae485c864188cf9aa0e4108b0b6934dc91e",
)
@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)])
@require_torch
def test_tokenizer_translation(self):
inputs = self.tokenizer._build_translation_inputs("A test", return_tensors="pt", src_lang="en", tgt_lang="ar")
self.assertEqual(
nested_simplify(inputs),
{
# en_XX, A, test, EOS
"input_ids": [[128022, 58, 4183, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 128006,
},
)