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# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# 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 logging
import unittest
from transformers import is_torch_available
from .utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_torch, slow
if is_torch_available():
from transformers import (
AutoConfig,
BertConfig,
AutoModel,
BertModel,
AutoModelForPreTraining,
BertForPreTraining,
AutoModelWithLMHead,
BertForMaskedLM,
RobertaForMaskedLM,
AutoModelForSequenceClassification,
BertForSequenceClassification,
AutoModelForQuestionAnswering,
BertForQuestionAnswering,
)
from transformers.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_MAP
from transformers.modeling_auto import (
MODEL_MAPPING,
MODEL_FOR_PRETRAINING_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
)
@require_torch
class AutoModelTest(unittest.TestCase):
@slow
def test_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModel.from_pretrained(model_name)
model, loading_info = AutoModel.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertModel)
for value in loading_info.values():
self.assertEqual(len(value), 0)
@slow
def test_model_for_pretraining_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForPreTraining.from_pretrained(model_name)
model, loading_info = AutoModelForPreTraining.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForPreTraining)
for value in loading_info.values():
self.assertEqual(len(value), 0)
@slow
def test_lmhead_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelWithLMHead.from_pretrained(model_name)
model, loading_info = AutoModelWithLMHead.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@slow
def test_sequence_classification_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
model, loading_info = AutoModelForSequenceClassification.from_pretrained(
model_name, output_loading_info=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForSequenceClassification)
# @slow
def test_question_answering_model_from_pretrained(self):
logging.basicConfig(level=logging.INFO)
for model_name in list(BERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]:
config = AutoConfig.from_pretrained(model_name)
self.assertIsNotNone(config)
self.assertIsInstance(config, BertConfig)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
model, loading_info = AutoModelForQuestionAnswering.from_pretrained(model_name, output_loading_info=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForQuestionAnswering)
def test_from_pretrained_identifier(self):
logging.basicConfig(level=logging.INFO)
model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
self.assertIsInstance(model, BertForMaskedLM)
self.assertEqual(model.num_parameters(), 14830)
self.assertEqual(model.num_parameters(only_trainable=True), 14830)
def test_from_identifier_from_model_type(self):
logging.basicConfig(level=logging.INFO)
model = AutoModelWithLMHead.from_pretrained(DUMMY_UNKWOWN_IDENTIFIER)
self.assertIsInstance(model, RobertaForMaskedLM)
self.assertEqual(model.num_parameters(), 14830)
self.assertEqual(model.num_parameters(only_trainable=True), 14830)
def test_parents_and_children_in_mappings(self):
# Test that the children are placed before the parents in the mappings, as the `instanceof` will be triggered
# by the parents and will return the wrong configuration type when using auto models
mappings = (
MODEL_MAPPING,
MODEL_FOR_PRETRAINING_MAPPING,
MODEL_FOR_QUESTION_ANSWERING_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
)
for mapping in mappings:
mapping = tuple(mapping.items())
for index, (child_config, child_model) in enumerate(mapping[1:]):
for parent_config, parent_model in mapping[: index + 1]:
with self.subTest(
msg="Testing if {} is child of {}".format(child_config.__name__, parent_config.__name__)
):
self.assertFalse(issubclass(child_config, parent_config))
self.assertFalse(issubclass(child_model, parent_model))
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