# 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 sys import tempfile import unittest from collections import OrderedDict from pathlib import Path import pytest from transformers import BertConfig, GPT2Model, is_safetensors_available, is_torch_available from transformers.models.auto.configuration_auto import CONFIG_MAPPING from transformers.testing_utils import ( DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, RequestCounter, require_torch, slow, ) from ..bert.test_modeling_bert import BertModelTester sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils")) from test_module.custom_configuration import CustomConfig # noqa E402 if is_torch_available(): import torch from test_module.custom_modeling import CustomModel from transformers import ( AutoConfig, AutoModel, AutoModelForCausalLM, AutoModelForMaskedLM, AutoModelForPreTraining, AutoModelForQuestionAnswering, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification, AutoModelForTableQuestionAnswering, AutoModelForTokenClassification, AutoModelWithLMHead, BertForMaskedLM, BertForPreTraining, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertModel, FunnelBaseModel, FunnelModel, GPT2Config, GPT2LMHeadModel, 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_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, MODEL_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 @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) # When using PyTorch checkpoint, the expected value is `8`. With `safetensors` checkpoint (if it is # installed), the expected value becomes `7`. EXPECTED_NUM_OF_UNEXPECTED_KEYS = 7 if is_safetensors_available() else 8 self.assertEqual(len(loading_info["unexpected_keys"]), EXPECTED_NUM_OF_UNEXPECTED_KEYS) 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. 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 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_from_pretrained_dynamic_model_local(self): try: AutoConfig.register("custom", CustomConfig) AutoModel.register(CustomConfig, CustomModel) config = CustomConfig(hidden_size=32) model = CustomModel(config) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) 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)) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] if CustomConfig in MODEL_MAPPING._extra_content: del MODEL_MAPPING._extra_content[CustomConfig] def test_from_pretrained_dynamic_model_distant(self): model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model", trust_remote_code=True) self.assertEqual(model.__class__.__name__, "NewModel") # Test model can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True) self.assertEqual(reloaded_model.__class__.__name__, "NewModel") for p1, p2 in zip(model.parameters(), reloaded_model.parameters()): self.assertTrue(torch.equal(p1, p2)) # This one uses a relative import to a util file, this checks it is downloaded and used properly. model = AutoModel.from_pretrained("hf-internal-testing/test_dynamic_model_with_util", trust_remote_code=True) self.assertEqual(model.__class__.__name__, "NewModel") # Test model can be reloaded. with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) reloaded_model = AutoModel.from_pretrained(tmp_dir, trust_remote_code=True) self.assertEqual(reloaded_model.__class__.__name__, "NewModel") for p1, p2 in zip(model.parameters(), reloaded_model.parameters()): self.assertTrue(torch.equal(p1, p2)) def test_new_model_registration(self): AutoConfig.register("custom", CustomConfig) 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, CustomModel) auto_class.register(CustomConfig, CustomModel) # 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 = CustomConfig(**tiny_config.to_dict()) model = auto_class.from_config(config) self.assertIsInstance(model, CustomModel) with tempfile.TemporaryDirectory() as tmp_dir: model.save_pretrained(tmp_dir) new_model = auto_class.from_pretrained(tmp_dir) # The model is a CustomModel but from the new dynamically imported class. self.assertIsInstance(new_model, CustomModel) finally: if "custom" in CONFIG_MAPPING._extra_content: del CONFIG_MAPPING._extra_content["custom"] 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 CustomConfig in mapping._extra_content: del mapping._extra_content[CustomConfig] def test_repo_not_found(self): with self.assertRaisesRegex( EnvironmentError, "bert-base is not a local folder and is not a valid model identifier" ): _ = AutoModel.from_pretrained("bert-base") def test_revision_not_found(self): with self.assertRaisesRegex( EnvironmentError, r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ): _ = AutoModel.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", ): _ = AutoModel.from_pretrained("hf-internal-testing/config-no-model") def test_model_from_tf_suggestion(self): with self.assertRaisesRegex(EnvironmentError, "Use `from_tf=True` to load this model"): _ = AutoModel.from_pretrained("hf-internal-testing/tiny-bert-tf-only") def test_model_from_flax_suggestion(self): with self.assertRaisesRegex(EnvironmentError, "Use `from_flax=True` to load this model"): _ = AutoModel.from_pretrained("hf-internal-testing/tiny-bert-flax-only") def test_cached_model_has_minimum_calls_to_head(self): # Make sure we have cached the model. _ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert") with RequestCounter() as counter: _ = AutoModel.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 _ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded") with RequestCounter() as counter: _ = AutoModel.from_pretrained("hf-internal-testing/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) def test_attr_not_existing(self): from transformers.models.auto.auto_factory import _LazyAutoMapping _CONFIG_MAPPING_NAMES = OrderedDict([("bert", "BertConfig")]) _MODEL_MAPPING_NAMES = OrderedDict([("bert", "GhostModel")]) _MODEL_MAPPING = _LazyAutoMapping(_CONFIG_MAPPING_NAMES, _MODEL_MAPPING_NAMES) with pytest.raises(ValueError, match=r"Could not find GhostModel neither in .* nor in .*!"): _MODEL_MAPPING[BertConfig] _MODEL_MAPPING_NAMES = OrderedDict([("bert", "BertModel")]) _MODEL_MAPPING = _LazyAutoMapping(_CONFIG_MAPPING_NAMES, _MODEL_MAPPING_NAMES) self.assertEqual(_MODEL_MAPPING[BertConfig], BertModel) _MODEL_MAPPING_NAMES = OrderedDict([("bert", "GPT2Model")]) _MODEL_MAPPING = _LazyAutoMapping(_CONFIG_MAPPING_NAMES, _MODEL_MAPPING_NAMES) self.assertEqual(_MODEL_MAPPING[BertConfig], GPT2Model)