# 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 require_torch, torch_device if is_torch_available(): from transformers import ( DistilBertConfig, DistilBertModel, DistilBertForMaskedLM, DistilBertForTokenClassification, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, ) @require_torch class DistilBertModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = ( (DistilBertModel, DistilBertForMaskedLM, DistilBertForQuestionAnswering, DistilBertForSequenceClassification) if is_torch_available() else None ) test_pruning = True test_torchscript = True test_resize_embeddings = True test_head_masking = True class DistilBertModelTester(object): def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=False, 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) 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 = DistilBertConfig( vocab_size=self.vocab_size, dim=self.hidden_size, n_layers=self.num_hidden_layers, n_heads=self.num_attention_heads, hidden_dim=self.intermediate_size, hidden_act=self.hidden_act, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, ) return config, input_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_distilbert_model( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DistilBertModel(config=config) model.to(torch_device) model.eval() (sequence_output,) = model(input_ids, input_mask) (sequence_output,) = model(input_ids) result = { "sequence_output": sequence_output, } self.parent.assertListEqual( list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size] ) def create_and_check_distilbert_for_masked_lm( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DistilBertForMaskedLM(config=config) model.to(torch_device) model.eval() loss, prediction_scores = model(input_ids, attention_mask=input_mask, 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_distilbert_for_question_answering( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = DistilBertForQuestionAnswering(config=config) model.to(torch_device) model.eval() loss, start_logits, end_logits = model( input_ids, attention_mask=input_mask, 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 create_and_check_distilbert_for_sequence_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = DistilBertForSequenceClassification(config) model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, labels=sequence_labels) result = { "loss": loss, "logits": logits, } self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_labels]) self.check_loss_output(result) def create_and_check_distilbert_for_token_classification( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_labels = self.num_labels model = DistilBertForTokenClassification(config=config) model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, 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 prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids, input_mask, sequence_labels, token_labels, choice_labels) = config_and_inputs inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask} return config, inputs_dict def setUp(self): self.model_tester = DistilBertModelTest.DistilBertModelTester(self) self.config_tester = ConfigTester(self, config_class=DistilBertConfig, dim=37) def test_config(self): self.config_tester.run_common_tests() def test_distilbert_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_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_distilbert_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_distilbert_for_question_answering(*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_distilbert_for_sequence_classification(*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_distilbert_for_token_classification(*config_and_inputs) # @slow # def test_model_from_pretrained(self): # for model_name in list(DISTILBERT_PRETRAINED_MODEL_ARCHIVE_MAP.keys())[:1]: # model = DistilBertModel.from_pretrained(model_name, cache_dir=CACHE_DIR) # self.assertIsNotNone(model)