# 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. import copy import os import tempfile import unittest from transformers import BertConfig, is_torch_available from transformers.models.auto.configuration_auto import CONFIG_MAPPING from transformers.testing_utils import ( DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_scatter, require_torch, slow, ) from .test_modeling_bert import BertModelTester if is_torch_available(): import torch from transformers import ( AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForMaskedLM, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForTableQuestionAnswering, AutoModelForTokenClassification, AutoModelWithLMHead, BertForMaskedLM, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertModel, FunnelBaseModel, FunnelModel, GPT2Config, GPT2LMHeadModel, PreTrainedModel, RobertaForMaskedLM, T5Config, T5ForConditionalGeneration, TapasConfig, TapasForQuestionAnswering, ) from transformers.models.auto.modeling_auto import ( MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, MODEL_FOR_PRETRAINING_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING, ) from transformers.models.bert.modeling_bert import BERT_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.gpt2.modeling_gpt2 import GPT2_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.t5.modeling_t5 import T5_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.models.tapas.modeling_tapas import TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST class NewModelConfig(BertConfig): model_type = "new-model" if is_torch_available(): class NewModel(BertModel): config_class = NewModelConfig class FakeModel(PreTrainedModel): config_class = BertConfig base_model_prefix = "fake" def __init__(self, config): super().__init__(config) self.linear = torch.nn.Linear(config.hidden_size, config.hidden_size) def forward(self, x): return self.linear(x) def _init_weights(self, module): pass # Make sure this is synchronized with the model above. FAKE_MODEL_CODE = """ import torch from transformers import BertConfig, PreTrainedModel class FakeModel(PreTrainedModel): config_class = BertConfig base_model_prefix = "fake" def __init__(self, config): super().__init__(config) self.linear = torch.nn.Linear(config.hidden_size, config.hidden_size) def forward(self, x): return self.linear(x) def _init_weights(self, module): pass """ @require_torch class AutoModelTest(unittest.TestCase): @slow def test_model_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[: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) self.assertEqual(len(loading_info["missing_keys"]), 0) self.assertEqual(len(loading_info["unexpected_keys"]), 8) self.assertEqual(len(loading_info["mismatched_keys"]), 0) self.assertEqual(len(loading_info["error_msgs"]), 0) @slow def test_model_for_pretraining_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[: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) # Only one value should not be initialized and in the missing keys. missing_keys = loading_info.pop("missing_keys") self.assertListEqual(["cls.predictions.decoder.bias"], missing_keys) for key, value in loading_info.items(): self.assertEqual(len(value), 0) @slow def test_lmhead_model_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[: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_model_for_causal_lm(self): for model_name in GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, GPT2Config) model = AutoModelForCausalLM.from_pretrained(model_name) model, loading_info = AutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, GPT2LMHeadModel) @slow def test_model_for_masked_lm(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = AutoModelForMaskedLM.from_pretrained(model_name) model, loading_info = AutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, BertForMaskedLM) @slow def test_model_for_encoder_decoder_lm(self): for model_name in T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, T5Config) model = AutoModelForSeq2SeqLM.from_pretrained(model_name) model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, T5ForConditionalGeneration) @slow def test_sequence_classification_model_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[: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): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[: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) @slow @require_scatter def test_table_question_answering_model_from_pretrained(self): for model_name in TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST[5:6]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, TapasConfig) model = AutoModelForTableQuestionAnswering.from_pretrained(model_name) model, loading_info = AutoModelForTableQuestionAnswering.from_pretrained( model_name, output_loading_info=True ) self.assertIsNotNone(model) self.assertIsInstance(model, TapasForQuestionAnswering) @slow def test_token_classification_model_from_pretrained(self): for model_name in BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: config = AutoConfig.from_pretrained(model_name) self.assertIsNotNone(config) self.assertIsInstance(config, BertConfig) model = AutoModelForTokenClassification.from_pretrained(model_name) model, loading_info = AutoModelForTokenClassification.from_pretrained(model_name, output_loading_info=True) self.assertIsNotNone(model) self.assertIsInstance(model, BertForTokenClassification) def test_from_pretrained_identifier(self): model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER) self.assertIsInstance(model, BertForMaskedLM) self.assertEqual(model.num_parameters(), 14410) self.assertEqual(model.num_parameters(only_trainable=True), 14410) def test_from_identifier_from_model_type(self): model = AutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER) self.assertIsInstance(model, RobertaForMaskedLM) 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 = AutoModel.from_pretrained("sgugger/funnel-random-tiny") self.assertIsInstance(model, FunnelModel) config = copy.deepcopy(model.config) config.architectures = ["FunnelBaseModel"] model = AutoModel.from_config(config) self.assertIsInstance(model, FunnelBaseModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) model = AutoModel.from_pretrained(tmp_dir) self.assertIsInstance(model, FunnelBaseModel) 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_TABLE_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_WITH_LM_HEAD_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, 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]: assert not issubclass( child_config, parent_config ), f"{child_config.__name__} is child of {parent_config.__name__}" # Tuplify child_model and parent_model since some of them could be tuples. if not isinstance(child_model, (list, tuple)): child_model = (child_model,) if not isinstance(parent_model, (list, tuple)): parent_model = (parent_model,) for child, parent in [(a, b) for a in child_model for b in parent_model]: assert not issubclass(child, parent), f"{child.__name__} is child of {parent.__name__}" def test_from_pretrained_dynamic_model(self): config = BertConfig( vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37 ) config.auto_map = {"AutoModel": "modeling.FakeModel"} model = FakeModel(config) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) with open(os.path.join(tmp_dir, "modeling.py"), "w") as f: f.write(FAKE_MODEL_CODE) new_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True) for p1, p2 in zip(model.parameters(), new_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_new_model_registration(self): AutoConfig.register("new-model", NewModelConfig) auto_classes = [ AutoModel, AutoModelForCausalLM, AutoModelForMaskedLM, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSequenceClassification, AutoModelForTokenClassification, ] try: 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, NewModel) auto_class.register(NewModelConfig, NewModel) # Trying to register something existing in the Transformers library will raise an error with self.assertRaises(ValueError): auto_class.register(BertConfig, BertModel) # 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, NewModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = auto_class.from_pretrained(tmp_dir) self.assertIsInstance(new_model, NewModel) finally: if "new-model" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["new-model"] for mapping in ( MODEL_MAPPING, MODEL_FOR_PRETRAINING_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_MASKED_LM_MAPPING, ): if NewModelConfig in mapping._extra_content: del mapping._extra_content[NewModelConfig]