# coding=utf-8 # Copyright 2020 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. from __future__ import annotations import copy import tempfile import unittest from transformers import CONFIG_MAPPING, AutoConfig, BertConfig, GPT2Config, T5Config, TapasConfig, is_tf_available from transformers.testing_utils import ( DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, RequestCounter, require_tensorflow_probability, require_tf, slow, ) from ..bert.test_modeling_bert import BertModelTester if is_tf_available(): from transformers import ( TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSeq2SeqLM, TFAutoModelForSequenceClassification, TFAutoModelForTableQuestionAnswering, TFAutoModelForTokenClassification, TFAutoModelWithLMHead, TFBertForMaskedLM, TFBertForPreTraining, TFBertForQuestionAnswering, TFBertForSequenceClassification, TFBertModel, TFFunnelBaseModel, TFFunnelModel, TFGPT2LMHeadModel, TFRobertaForMaskedLM, TFT5ForConditionalGeneration, TFTapasForQuestionAnswering, ) from transformers.models.auto.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_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, TF_MODEL_MAPPING, ) from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.gpt2.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.t5.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.tapas.modeling_tf_tapas import TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST class NewModelConfig(BertConfig): model_type = "new-model" if is_tf_available(): class TFNewModel(TFBertModel): config_class = NewModelConfig @require_tf class TFAutoModelTest(unittest.TestCase): @slow def test_model_from_pretrained(self): model_name = "bert-base-cased" 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): model_name = "bert-base-cased" 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) @slow @require_tensorflow_probability def test_table_question_answering_model_from_pretrained(self): for model_name in TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, TapasConfig) model = TFAutoModelForTableQuestionAnswering.from_pretrained(model_name) model, loading_info = TFAutoModelForTableQuestionAnswering.from_pretrained( model_name, output_loading_info=True ) self.assertIsNotNone(model) self.assertIsInstance(model, TFTapasForQuestionAnswering) def test_from_pretrained_identifier(self): model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(model, TFBertForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_identifier_from_model_type(self): model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER) self.assertIsInstance(model, TFRobertaForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_pretrained_with_tuple_values(self): # For the auto model mapping, FunnelConfig has two models: FunnelModel and FunnelBaseModel model = TFAutoModel.from_pretrained("sgugger/funnel-random-tiny") self.assertIsInstance(model, TFFunnelModel) config = copy.deepcopy(model.config) config.architectures = ["FunnelBaseModel"] model = TFAutoModel.from_config(config) self.assertIsInstance(model, TFFunnelBaseModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) model = TFAutoModel.from_pretrained(tmp_dir) self.assertIsInstance(model, TFFunnelBaseModel) def test_new_model_registration(self): try: AutoConfig.register("new-model", NewModelConfig) auto_classes = [ TFAutoModel, TFAutoModelForCausalLM, TFAutoModelForMaskedLM, TFAutoModelForPreTraining, TFAutoModelForQuestionAnswering, TFAutoModelForSequenceClassification, TFAutoModelForTokenClassification, ] for auto_class in auto_classes: with self.subTest(auto_class.__name__): # Wrong config class will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFNewModel) auto_class.register(NewModelConfig, TFNewModel) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, TFBertModel) # Now that the config is registered, it can be used as any other config with the auto-API tiny_config = BertModelTester(self).get_config() config = NewModelConfig(**tiny_config.to_dict()) model = auto_class.from_config(config) self.assertIsInstance(model, TFNewModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = auto_class.from_pretrained(tmp_dir) self.assertIsInstance(new_model, TFNewModel) finally: if "new-model" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["new-model"] for mapping in ( 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_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, ): if NewModelConfig in mapping._extra_content: del mapping._extra_content[NewModelConfig] def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = TFAutoModel.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = TFAutoModel.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, revision="aaaaaa") def test_model_file_not_found(self): with self.assertRaisesRegex( EnvironmentError, "hf-internal-testing/config-no-model does not appear to have a file named pytorch_model.bin", ): _ = TFAutoModel.from_pretrained("hf-internal-testing/config-no-model") def test_model_from_pt_suggestion(self): with self.assertRaisesRegex(EnvironmentError, "Use `from_pt=True` to load this model"): _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-bert-pt-only") def test_cached_model_has_minimum_calls_to_head(self): # Make sure we have cached the model. _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0) # With a sharded checkpoint _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") with RequestCounter() as counter: _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded") self.assertEqual(counter.get_request_count, 0) self.assertEqual(counter.head_request_count, 1) self.assertEqual(counter.other_request_count, 0)