# Copyright 2018 Microsoft Authors and the HuggingFace Inc. team. # # 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 DebertaV2Config, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( DebertaV2ForMaskedLM, DebertaV2ForMultipleChoice, DebertaV2ForQuestionAnswering, DebertaV2ForSequenceClassification, DebertaV2ForTokenClassification, DebertaV2Model, ) class DebertaV2ModelTester: 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=2, 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, relative_attention=False, position_biased_input=True, pos_att_type="None", 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.relative_attention = relative_attention self.position_biased_input = position_biased_input self.pos_att_type = pos_att_type 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 = self.get_config() return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return DebertaV2Config( 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, relative_attention=self.relative_attention, position_biased_input=self.position_biased_input, pos_att_type=self.pos_att_type, ) def check_loss_output(self, result): self.parent.assertListEqual(list(result.loss.size()), []) def create_and_check_deberta_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DebertaV2Model(config=config) model.to(torch_device) model.eval() sequence_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)[0] sequence_output = model(input_ids, token_type_ids=token_type_ids)[0] sequence_output = model(input_ids)[0] self.parent.assertListEqual(list(sequence_output.size()), [self.batch_size, self.seq_length, self.hidden_size]) def create_and_check_deberta_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DebertaV2ForMaskedLM(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_deberta_for_sequence_classification( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = DebertaV2ForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels) self.parent.assertListEqual(list(result.logits.size()), [self.batch_size, self.num_labels]) self.check_loss_output(result) def create_and_check_deberta_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 = DebertaV2ForTokenClassification(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_deberta_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DebertaV2ForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def create_and_check_deberta_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DebertaV2ForMultipleChoice(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() result = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices)) 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 @require_torch class DebertaV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DebertaV2Model, DebertaV2ForMaskedLM, DebertaV2ForSequenceClassification, DebertaV2ForTokenClassification, DebertaV2ForQuestionAnswering, DebertaV2ForMultipleChoice, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "feature-extraction": DebertaV2Model, "fill-mask": DebertaV2ForMaskedLM, "question-answering": DebertaV2ForQuestionAnswering, "text-classification": DebertaV2ForSequenceClassification, "token-classification": DebertaV2ForTokenClassification, "zero-shot": DebertaV2ForSequenceClassification, } if is_torch_available() else {} ) is_encoder_decoder = False def setUp(self): self.model_tester = DebertaV2ModelTester(self) self.config_tester = ConfigTester(self, config_class=DebertaV2Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_deberta_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deberta_model(*config_and_inputs) def test_for_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_deberta_for_sequence_classification(*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_deberta_for_masked_lm(*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_deberta_for_question_answering(*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_deberta_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_deberta_for_multiple_choice(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "microsoft/deberta-v2-xlarge" model = DebertaV2Model.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch @require_sentencepiece @require_tokenizers class DebertaV2ModelIntegrationTest(unittest.TestCase): @unittest.skip(reason="Model not available yet") def test_inference_masked_lm(self): pass @slow def test_inference_no_head(self): model = DebertaV2Model.from_pretrained("microsoft/deberta-v2-xlarge") input_ids = torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]) attention_mask = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]) with torch.no_grad(): output = model(input_ids, attention_mask=attention_mask)[0] # compare the actual values for a slice. expected_slice = torch.tensor( [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]] ) torch.testing.assert_close(output[:, 1:4, 1:4], expected_slice, rtol=1e-4, atol=1e-4)