# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # 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 from transformers import is_torch_available from .test_configuration_common import ConfigTester from .test_modeling_common import ModelTesterMixin, ids_tensor from .utils import CACHE_DIR, require_torch, slow, torch_device if is_torch_available(): import torch from transformers import ( RobertaConfig, RobertaModel, RobertaForMaskedLM, RobertaForSequenceClassification, RobertaForTokenClassification, ) from transformers.modeling_roberta import RobertaEmbeddings, RobertaForMultipleChoice, RobertaForQuestionAnswering from transformers.modeling_roberta import ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP from transformers.modeling_utils import create_position_ids_from_input_ids @require_torch class RobertaModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (RobertaForMaskedLM, RobertaModel) if is_torch_available() else () class RobertaModelTester(object): def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=32, num_hidden_layers=5, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, scope=None, ): 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.use_labels = use_labels self.vocab_size = vocab_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.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.num_labels = num_labels self.num_choices = num_choices self.scope = scope 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 = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) 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) sequence_labels = None token_labels = None choice_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels) choice_labels = ids_tensor([self.batch_size], self.num_choices) config = RobertaConfig( vocab_size=self.vocab_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, ) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def check_loss_output(self, result): self.parent.assertListEqual(list(result["loss"].size()), []) def create_and_check_roberta_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RobertaModel(config=config) model.to(torch_device) model.eval() sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids) sequence_output, pooled_output = model(input_ids) result = { "sequence_output": sequence_output, "pooled_output": pooled_output, } self.parent.assertListEqual( list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size] ) self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size]) def create_and_check_roberta_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RobertaForMaskedLM(config=config) model.to(torch_device) model.eval() loss, prediction_scores = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, masked_lm_labels=token_labels ) result = { "loss": loss, "prediction_scores": prediction_scores, } self.parent.assertListEqual( list(result["prediction_scores"].size()), [self.batch_size, self.seq_length, self.vocab_size] ) self.check_loss_output(result) def create_and_check_roberta_for_token_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = RobertaForTokenClassification(config=config) model.to(torch_device) model.eval() loss, logits = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels ) result = { "loss": loss, "logits": logits, } self.parent.assertListEqual( list(result["logits"].size()), [self.batch_size, self.seq_length, self.num_labels] ) self.check_loss_output(result) def create_and_check_roberta_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = RobertaForMultipleChoice(config=config) model.to(torch_device) model.eval() multiple_choice_inputs_ids = input_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_token_type_ids = token_type_ids.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() multiple_choice_input_mask = input_mask.unsqueeze(1).expand(-1, self.num_choices, -1).contiguous() loss, logits = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) result = { "loss": loss, "logits": logits, } self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices]) self.check_loss_output(result) def create_and_check_roberta_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = RobertaForQuestionAnswering(config=config) model.to(torch_device) model.eval() loss, start_logits, end_logits = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) result = { "loss": loss, "start_logits": start_logits, "end_logits": end_logits, } self.parent.assertListEqual(list(result["start_logits"].size()), [self.batch_size, self.seq_length]) self.parent.assertListEqual(list(result["end_logits"].size()), [self.batch_size, self.seq_length]) self.check_loss_output(result) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels, ) = config_and_inputs inputs_dict = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask} return config, inputs_dict def setUp(self): self.model_tester = RobertaModelTest.RobertaModelTester(self) self.config_tester = ConfigTester(self, config_class=RobertaConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_roberta_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_roberta_model(*config_and_inputs) def test_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_roberta_for_masked_lm(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_roberta_for_token_classification(*config_and_inputs) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_roberta_for_multiple_choice(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_roberta_for_question_answering(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in list(ROBERTA_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: model = RobertaModel.from_pretrained(model_name, cache_dir=CACHE_DIR) self.assertIsNotNone(model) def test_create_position_ids_respects_padding_index(self): """ Ensure that the default position ids only assign a sequential . This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is RobertaEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] model = RobertaEmbeddings(config=config) input_ids = torch.as_tensor([[12, 31, 13, model.padding_idx]]) expected_positions = torch.as_tensor( [[0 + model.padding_idx + 1, 1 + model.padding_idx + 1, 2 + model.padding_idx + 1, model.padding_idx]] ) position_ids = create_position_ids_from_input_ids(input_ids, model.padding_idx) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) def test_create_position_ids_from_inputs_embeds(self): """ Ensure that the default position ids only assign a sequential . This is a regression test for https://github.com/huggingface/transformers/issues/1761 The position ids should be masked with the embedding object's padding index. Therefore, the first available non-padding position index is RobertaEmbeddings.padding_idx + 1 """ config = self.model_tester.prepare_config_and_inputs()[0] embeddings = RobertaEmbeddings(config=config) inputs_embeds = torch.Tensor(2, 4, 30) expected_single_positions = [ 0 + embeddings.padding_idx + 1, 1 + embeddings.padding_idx + 1, 2 + embeddings.padding_idx + 1, 3 + embeddings.padding_idx + 1, ] expected_positions = torch.as_tensor([expected_single_positions, expected_single_positions]) position_ids = embeddings.create_position_ids_from_inputs_embeds(inputs_embeds) self.assertEqual(position_ids.shape, expected_positions.shape) self.assertTrue(torch.all(torch.eq(position_ids, expected_positions))) class RobertaModelIntegrationTest(unittest.TestCase): @slow def test_inference_masked_lm(self): model = RobertaForMaskedLM.from_pretrained("roberta-base") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 50265)) self.assertEqual(output.shape, expected_shape) # compare the actual values for a slice. expected_slice = torch.Tensor( [[[33.8843, -4.3107, 22.7779], [4.6533, -2.8099, 13.6252], [1.8222, -3.6898, 8.8600]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)) @slow def test_inference_no_head(self): model = RobertaModel.from_pretrained("roberta-base") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] # compare the actual values for a slice. expected_slice = torch.Tensor( [[[-0.0231, 0.0782, 0.0074], [-0.1854, 0.0539, -0.0174], [0.0548, 0.0799, 0.1687]]] ) self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-3)) @slow def test_inference_classification_head(self): model = RobertaForSequenceClassification.from_pretrained("roberta-large-mnli") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) output = model(input_ids)[0] expected_shape = torch.Size((1, 3)) self.assertEqual(output.shape, expected_shape) expected_tensor = torch.Tensor([[-0.9469, 0.3913, 0.5118]]) self.assertTrue(torch.allclose(output, expected_tensor, atol=1e-3))