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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.
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, 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,
TFGPT2LMHeadModel,
TFRobertaForMaskedLM,
TFT5ForConditionalGeneration,
)
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
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeq2SeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPT2LMHeadModel,
RobertaForMaskedLM,
T5ForConditionalGeneration,
)
@is_pt_tf_cross_test
class TFPTAutoModelTest(unittest.TestCase):
@slow
def test_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 = TFAutoModel.from_pretrained(model_name, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertModel)
model = AutoModel.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertModel)
@slow
def test_model_for_pretraining_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 = TFAutoModelForPreTraining.from_pretrained(model_name, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForPreTraining)
model = AutoModelForPreTraining.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForPreTraining)
@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, from_pt=True)
model, loading_info = TFAutoModelForCausalLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFGPT2LMHeadModel)
model = AutoModelForCausalLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForCausalLM.from_pretrained(
model_name, output_loading_info=True, from_tf=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, GPT2LMHeadModel)
@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, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = AutoModelWithLMHead.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@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, from_pt=True)
model, loading_info = TFAutoModelForMaskedLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForMaskedLM)
model = AutoModelForMaskedLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForMaskedLM.from_pretrained(
model_name, output_loading_info=True, from_tf=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForMaskedLM)
@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, from_pt=True)
model, loading_info = TFAutoModelForSeq2SeqLM.from_pretrained(
model_name, output_loading_info=True, from_pt=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFT5ForConditionalGeneration)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name, from_tf=True)
model, loading_info = AutoModelForSeq2SeqLM.from_pretrained(
model_name, output_loading_info=True, from_tf=True
)
self.assertIsNotNone(model)
self.assertIsInstance(model, T5ForConditionalGeneration)
@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, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForSequenceClassification)
model = AutoModelForSequenceClassification.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForSequenceClassification)
@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, from_pt=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, TFBertForQuestionAnswering)
model = AutoModelForQuestionAnswering.from_pretrained(model_name, from_tf=True)
self.assertIsNotNone(model)
self.assertIsInstance(model, BertForQuestionAnswering)
def test_from_pretrained_identifier(self):
model = TFAutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER, from_pt=True)
self.assertIsInstance(model, TFBertForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
model = AutoModelWithLMHead.from_pretrained(SMALL_MODEL_IDENTIFIER, from_tf=True)
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 = TFAutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, from_pt=True)
self.assertIsInstance(model, TFRobertaForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
model = AutoModelWithLMHead.from_pretrained(DUMMY_UNKNOWN_IDENTIFIER, from_tf=True)
self.assertIsInstance(model, RobertaForMaskedLM)
self.assertEqual(model.num_parameters(), 14410)
self.assertEqual(model.num_parameters(only_trainable=True), 14410)
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