# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved. # 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 unittest import paddle from paddlenlp.transformers import ( RemBertConfig, RemBertForMaskedLM, RemBertForMultipleChoice, RemBertForQuestionAnswering, RemBertForSequenceClassification, RemBertForTokenClassification, RemBertModel, RemBertPretrainedModel, ) from ...testing_utils import slow from ..test_modeling_common import ModelTesterMixin, ids_tensor class RemBertModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, vocab_size=99, input_embedding_size=64, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0, attention_probs_dropout_prob=0, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, pad_token_id=0, layer_norm_eps=1e-12, num_classes=2, num_choices=1, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.vocab_size = vocab_size self.input_embedding_size = input_embedding_size self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.initializer_range = initializer_range self.pad_token_id = pad_token_id self.layer_norm_eps = layer_norm_eps self.num_classes = num_classes self.num_choices = num_choices def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = paddle.ones([self.batch_size, self.seq_length], dtype="int32") token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size) config = self.get_config() return config, input_ids, token_type_ids, input_mask def get_config(self): return RemBertConfig( vocab_size=self.vocab_size, input_embedding_size=self.input_embedding_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, pad_token_id=self.pad_token_id, layer_norm_eps=self.layer_norm_eps, num_classes=self.num_classes, num_choices=self.num_choices, ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids, input_mask, token_type_ids) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids, } return config, inputs_dict def create_and_check_model( self, config, input_ids, token_type_ids, input_mask, ): model = RemBertModel(config) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) self.parent.assertEqual(result[1].shape, [self.batch_size, self.hidden_size]) def create_and_check_masked_lm_model( self, config, input_ids, token_type_ids, input_mask, ): model = RemBertForMaskedLM(config) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.shape, [self.batch_size, self.seq_length, self.hidden_size]) def create_and_check_question_answering_model( self, config, input_ids, token_type_ids, input_mask, ): model = RemBertForQuestionAnswering(config) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, 1]) self.parent.assertEqual(result[1].shape, [self.batch_size, self.seq_length, 1]) def create_and_check_sequence_classification_model( self, config, input_ids, token_type_ids, input_mask, ): model = RemBertForSequenceClassification(config) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.shape, [self.batch_size, self.num_classes]) def create_and_check_multiple_choice_model( self, config, input_ids, token_type_ids, input_mask, ): model = RemBertForMultipleChoice(config) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.shape, [self.batch_size, self.num_choices]) def create_and_check_token_classification_model( self, config, input_ids, token_type_ids, input_mask, ): model = RemBertForTokenClassification(config) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) self.parent.assertEqual(result.shape, [self.batch_size, self.seq_length, self.num_classes]) class RemBertModelTest(ModelTesterMixin, unittest.TestCase): base_model_class = RemBertModel return_dict: bool = False use_labels: bool = False use_test_inputs_embeds: bool = False all_model_classes = ( RemBertModel, RemBertForMaskedLM, RemBertForQuestionAnswering, RemBertForSequenceClassification, RemBertForMultipleChoice, RemBertForTokenClassification, ) def setUp(self): self.model_tester = RemBertModelTester(self) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_masked_lm_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_masked_lm_model(*config_and_inputs) def test_question_answering_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_question_answering_model(*config_and_inputs) def test_sequence_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_sequence_classification_model(*config_and_inputs) def test_multiple_choice_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_multiple_choice_model(*config_and_inputs) def test_token_classification_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_token_classification_model(*config_and_inputs) @slow @unittest.skip("Skip for miss model weight.") def test_model_from_pretrained(self): for model_name in list(RemBertPretrainedModel.pretrained_init_configuration)[:1]: model = RemBertModel.from_pretrained(model_name) self.assertIsNotNone(model)