[Test refactor 1/5] Per-folder tests reorganization (#15725)
* Per-folder tests reorganization Co-authored-by: sgugger <sylvain.gugger@gmail.com> Co-authored-by: Stas Bekman <stas@stason.org>
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Lysandre Debut
324
tests/auto/test_modeling_tf_auto.py
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324
tests/auto/test_modeling_tf_auto.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import copy
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import tempfile
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import unittest
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from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPT2Config, T5Config, TapasConfig, is_tf_available
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from transformers.testing_utils import (
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DUMMY_UNKNOWN_IDENTIFIER,
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SMALL_MODEL_IDENTIFIER,
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require_tensorflow_probability,
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require_tf,
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slow,
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)
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from ..bert.test_modeling_bert import BertModelTester
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if is_tf_available():
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from transformers import (
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TFAutoModel,
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TFAutoModelForCausalLM,
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TFAutoModelForMaskedLM,
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TFAutoModelForPreTraining,
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TFAutoModelForQuestionAnswering,
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TFAutoModelForSeq2SeqLM,
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TFAutoModelForSequenceClassification,
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TFAutoModelForTableQuestionAnswering,
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TFAutoModelForTokenClassification,
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TFAutoModelWithLMHead,
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TFBertForMaskedLM,
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TFBertForPreTraining,
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TFBertForQuestionAnswering,
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TFBertForSequenceClassification,
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TFBertModel,
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TFFunnelBaseModel,
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TFFunnelModel,
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TFGPT2LMHeadModel,
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TFRobertaForMaskedLM,
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TFT5ForConditionalGeneration,
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TFTapasForQuestionAnswering,
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)
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from transformers.models.auto.modeling_tf_auto import (
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TF_MODEL_FOR_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_MASKED_LM_MAPPING,
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TF_MODEL_FOR_PRETRAINING_MAPPING,
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TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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TF_MODEL_MAPPING,
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TF_MODEL_WITH_LM_HEAD_MAPPING,
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)
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from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST
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class NewModelConfig(BertConfig):
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model_type = "new-model"
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if is_tf_available():
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class TFNewModel(TFBertModel):
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config_class = NewModelConfig
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@require_tf
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class TFAutoModelTest(unittest.TestCase):
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@slow
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def test_model_from_pretrained(self):
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model_name = "bert-base-cased"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertModel)
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@slow
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def test_model_for_pretraining_from_pretrained(self):
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model_name = "bert-base-cased"
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForPreTraining.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForPreTraining)
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@slow
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def test_model_for_causal_lm(self):
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for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, GPT2Config)
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model = TFAutoModelForCausalLM.from_pretrained(model_name)
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model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFGPT2LMHeadModel)
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@slow
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def test_lmhead_model_from_pretrained(self):
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for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelWithLMHead.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForMaskedLM)
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@slow
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def test_model_for_masked_lm(self):
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for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForMaskedLM.from_pretrained(model_name)
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model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForMaskedLM)
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@slow
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def test_model_for_encoder_decoder_lm(self):
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for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, T5Config)
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model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
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model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFT5ForConditionalGeneration)
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@slow
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def test_sequence_classification_model_from_pretrained(self):
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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForSequenceClassification)
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@slow
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def test_question_answering_model_from_pretrained(self):
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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForQuestionAnswering)
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@slow
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@require_tensorflow_probability
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def test_table_question_answering_model_from_pretrained(self):
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for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, TapasConfig)
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model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name)
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model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained(
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model_name, output_loading_info=True
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)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFTapasForQuestionAnswering)
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def test_from_pretrained_identifier(self):
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model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
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self.assertIsInstance(model, TFBertForMaskedLM)
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self.assertEqual(model.num_parameters(), 14410)
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self.assertEqual(model.num_parameters(only_trainable=True), 14410)
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def test_from_identifier_from_model_type(self):
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model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER)
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self.assertIsInstance(model, TFRobertaForMaskedLM)
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self.assertEqual(model.num_parameters(), 14410)
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self.assertEqual(model.num_parameters(only_trainable=True), 14410)
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def test_from_pretrained_with_tuple_values(self):
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# For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel
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model = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny")
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self.assertIsInstance(model, TFFunnelModel)
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config = copy.deepcopy(model.config)
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config.architectures = ["FunnelBaseModel"]
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model = TFAutoModel.from_config(config)
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self.assertIsInstance(model, TFFunnelBaseModel)
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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model = TFAutoModel.from_pretrained(tmp_dir)
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self.assertIsInstance(model, TFFunnelBaseModel)
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def test_parents_and_children_in_mappings(self):
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# Test that the children are placed before the parents in the mappings, as the `instanceof` will be triggered
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# by the parents and will return the wrong configuration type when using auto models
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mappings = (
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TF_MODEL_MAPPING,
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TF_MODEL_FOR_PRETRAINING_MAPPING,
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TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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TF_MODEL_WITH_LM_HEAD_MAPPING,
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TF_MODEL_FOR_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_MASKED_LM_MAPPING,
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TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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)
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for mapping in mappings:
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mapping = tuple(mapping.items())
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for index, (child_config, child_model) in enumerate(mapping[1:]):
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for parent_config, parent_model in mapping[: index + 1]:
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with self.subTest(msg=f"Testing if {child_config.__name__} is child of {parent_config.__name__}"):
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self.assertFalse(issubclass(child_config, parent_config))
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# Tuplify child_model and parent_model since some of them could be tuples.
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if not isinstance(child_model, (list, tuple)):
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child_model = (child_model,)
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if not isinstance(parent_model, (list, tuple)):
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parent_model = (parent_model,)
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for child, parent in [(a, b) for a in child_model for b in parent_model]:
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assert not issubclass(child, parent), f"{child.__name__} is child of {parent.__name__}"
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def test_new_model_registration(self):
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try:
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AutoConfig.register("new-model", NewModelConfig)
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auto_classes = [
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TFAutoModel,
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TFAutoModelForCausalLM,
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TFAutoModelForMaskedLM,
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TFAutoModelForPreTraining,
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TFAutoModelForQuestionAnswering,
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TFAutoModelForSequenceClassification,
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TFAutoModelForTokenClassification,
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]
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for auto_class in auto_classes:
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with self.subTest(auto_class.__name__):
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# Wrong config class will raise an error
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with self.assertRaises(ValueError):
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auto_class.register(BertConfig, TFNewModel)
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auto_class.register(NewModelConfig, TFNewModel)
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# Trying to register something existing in the Transformers library will raise an error
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with self.assertRaises(ValueError):
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auto_class.register(BertConfig, TFBertModel)
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# Now that the config is registered, it can be used as any other config with the auto-API
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tiny_config = BertModelTester(self).get_config()
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config = NewModelConfig(**tiny_config.to_dict())
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model = auto_class.from_config(config)
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self.assertIsInstance(model, TFNewModel)
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with tempfile.TemporaryDirectory() as tmp_dir:
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model.save_pretrained(tmp_dir)
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new_model = auto_class.from_pretrained(tmp_dir)
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self.assertIsInstance(new_model, TFNewModel)
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finally:
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if "new-model" in CONFIG_MAPPING._extra_content:
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del CONFIG_MAPPING._extra_content["new-model"]
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for mapping in (
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TF_MODEL_MAPPING,
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TF_MODEL_FOR_PRETRAINING_MAPPING,
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TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_MASKED_LM_MAPPING,
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):
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if NewModelConfig in mapping._extra_content:
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del mapping._extra_content[NewModelConfig]
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def test_repo_not_found(self):
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with self.assertRaisesRegex(
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EnvironmentError, "bert-base is not a local folder and is not a valid model identifier"
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):
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_ = TFAutoModel.from_pretrained("bert-base")
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def test_revision_not_found(self):
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with self.assertRaisesRegex(
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EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"
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):
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_ = TFAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa")
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def test_model_file_not_found(self):
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with self.assertRaisesRegex(
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EnvironmentError,
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"hf-internal-testing/config-no-model does not appear to have a file named tf_model.h5",
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):
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_ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model")
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def test_model_from_pt_suggestion(self):
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with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"):
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_ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only")
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