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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, | |
) | |
class TFPTAutoModelTest(unittest.TestCase): | |
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) | |
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) | |
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) | |
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) | |
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) | |
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) | |
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) | |
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) | |