203 строки
8.3 KiB
Python
203 строки
8.3 KiB
Python
# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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from transformers import is_tf_available
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from transformers.testing_utils import DUMMY_UNKWOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, require_tf, slow
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if is_tf_available():
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from transformers import (
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AutoConfig,
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BertConfig,
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GPT2Config,
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T5Config,
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TFAutoModel,
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TFBertModel,
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TFAutoModelForPreTraining,
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TFBertForPreTraining,
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TFAutoModelWithLMHead,
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TFBertForMaskedLM,
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TFRobertaForMaskedLM,
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TFAutoModelForSequenceClassification,
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TFBertForSequenceClassification,
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TFAutoModelForQuestionAnswering,
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TFBertForQuestionAnswering,
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TFAutoModelForCausalLM,
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TFGPT2LMHeadModel,
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TFAutoModelForMaskedLM,
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TFAutoModelForSeq2SeqLM,
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TFT5ForConditionalGeneration,
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)
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from transformers.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.modeling_tf_gpt2 import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.modeling_tf_t5 import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
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from transformers.modeling_tf_auto import (
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TF_MODEL_MAPPING,
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TF_MODEL_FOR_PRETRAINING_MAPPING,
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TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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TF_MODEL_WITH_LM_HEAD_MAPPING,
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TF_MODEL_FOR_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_MASKED_LM_MAPPING,
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TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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)
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@require_tf
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class TFAutoModelTest(unittest.TestCase):
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@slow
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def test_model_from_pretrained(self):
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import h5py
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self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertModel)
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@slow
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def test_model_for_pretraining_from_pretrained(self):
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import h5py
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self.assertTrue(h5py.version.hdf5_version.startswith("1.10"))
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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForPreTraining.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForPreTraining)
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@slow
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def test_model_for_causal_lm(self):
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for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, GPT2Config)
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model = TFAutoModelForCausalLM.from_pretrained(model_name)
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model, loading_info = TFAutoModelForCausalLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFGPT2LMHeadModel)
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@slow
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def test_lmhead_model_from_pretrained(self):
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for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelWithLMHead.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForMaskedLM)
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@slow
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def test_model_for_masked_lm(self):
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for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForMaskedLM.from_pretrained(model_name)
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model, loading_info = TFAutoModelForMaskedLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForMaskedLM)
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@slow
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def test_model_for_encoder_decoder_lm(self):
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for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, T5Config)
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model = TFAutoModelForSeq2SeqLM.from_pretrained(model_name)
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model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(model_name, output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFT5ForConditionalGeneration)
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@slow
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def test_sequence_classification_model_from_pretrained(self):
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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForSequenceClassification.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForSequenceClassification)
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@slow
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def test_question_answering_model_from_pretrained(self):
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# for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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for model_name in ["bert-base-uncased"]:
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config = AutoConfig.from_pretrained(model_name)
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self.assertIsNotNone(config)
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self.assertIsInstance(config, BertConfig)
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model = TFAutoModelForQuestionAnswering.from_pretrained(model_name)
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self.assertIsNotNone(model)
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self.assertIsInstance(model, TFBertForQuestionAnswering)
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def test_from_pretrained_identifier(self):
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model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER)
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self.assertIsInstance(model, TFBertForMaskedLM)
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self.assertEqual(model.num_parameters(), 14830)
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self.assertEqual(model.num_parameters(only_trainable=True), 14830)
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def test_from_identifier_from_model_type(self):
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model = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKWOWN_IDENTIFIER)
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self.assertIsInstance(model, TFRobertaForMaskedLM)
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self.assertEqual(model.num_parameters(), 14830)
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self.assertEqual(model.num_parameters(only_trainable=True), 14830)
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def test_parents_and_children_in_mappings(self):
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# Test that the children are placed before the parents in the mappings, as the `instanceof` will be triggered
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# by the parents and will return the wrong configuration type when using auto models
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mappings = (
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TF_MODEL_MAPPING,
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TF_MODEL_FOR_PRETRAINING_MAPPING,
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TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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TF_MODEL_WITH_LM_HEAD_MAPPING,
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TF_MODEL_FOR_CAUSAL_LM_MAPPING,
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TF_MODEL_FOR_MASKED_LM_MAPPING,
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TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
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)
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for mapping in mappings:
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mapping = tuple(mapping.items())
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for index, (child_config, child_model) in enumerate(mapping[1:]):
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for parent_config, parent_model in mapping[: index + 1]:
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with self.subTest(
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msg="Testing if {} is child of {}".format(child_config.__name__, parent_config.__name__)
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):
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self.assertFalse(issubclass(child_config, parent_config))
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self.assertFalse(issubclass(child_model, parent_model))
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