# 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 unittest from transformers import is_tf_available from transformers.testing_utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_tf, slow if is_tf_available(): from transformers import ( AutoConfig, BertConfig, GPT2Config, T5Config, TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelWithLMHead, TFBertForMaskedLM, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertModel, TFGPT2LMHeadModel, TFRobertaForMaskedLM, TFT5ForConditionalGeneration, ) from transformers.modeling_tf_auto import ( TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING, ) from transformers.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST @require_tf class TFAutoModelTest(unittest.TestCase): @slow def test_model_from_pretrained(self): import h5py self.assertTrue(h5py.version.hdf5_version.startswith("1.10")) # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModel.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertModel) @slow def test_model_for_pretraining_from_pretrained(self): import h5py self.assertTrue(h5py.version.hdf5_version.startswith("1.10")) # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForPreTraining.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForPreTraining) @slow def test_model_for_causal_lm(self): for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, GPT2Config) model = TFAutoModelForCausalLM.from_pretrained(model_name) model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFGPT2LMHeadModel) @slow def test_lmhead_model_from_pretrained(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelWithLMHead.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_masked_lm(self): for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForMaskedLM.from_pretrained(model_name) model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForMaskedLM) @slow def test_model_for_encoder_decoder_lm(self): for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, T5Config) model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name) model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, TFT5ForConditionalGeneration) @slow def test_sequence_classification_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForSequenceClassification.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForSequenceClassification) @slow def test_question_answering_model_from_pretrained(self): # for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: for model_name in ["bert-base-uncased"]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = TFAutoModelForQuestionAnswering.from_pretrained(model_name) self.assertIsNotNone(model) self.assertIsInstance(model, TFBertForQuestionAnswering) def test_from_pretrained_identifier(self): model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(model, TFBertForMaskedLM) self.assertEqual(model.num_parameters(), 14830) self.assertEqual(model.num_parameters(only_trainable=True), 14830) def test_from_identifier_from_model_type(self): model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKWOWN_IDENTIFIER) self.assertIsInstance(model, TFRobertaForMaskedLM) 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 = ( TF_MODEL_MAPPING, TF_MODEL_FOR_PRETRAINING_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_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))