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- # 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 tempfile
- 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,
- TFFunnelBaseModel,
- TFFunnelModel,
- TFGPT2LMHeadModel,
- TFRobertaForMaskedLM,
- TFT5ForConditionalGeneration,
- )
- 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_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.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
-
-
- @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(), 14410)
- self.assertEqual(model.num_parameters(only_trainable=True), 14410)
-
- def test_from_identifier_from_model_type(self):
- model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKWOWN_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_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=f"Testing if {child_config.__name__} is child of {parent_config.__name__}"):
- self.assertFalse(issubclass(child_config, parent_config))
-
- # 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__}"
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