# 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. import os import tempfile import unittest from transformers import DistilBertConfig, is_torch_available from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST, DistilBertForMaskedLM, DistilBertForMultipleChoice, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, DistilBertForTokenClassification, DistilBertModel, ) 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 = random_attention_mask([self.batch_size, self.seq_length]) 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, input_mask, sequence_labels, token_labels, choice_labels def get_config(self): return 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, ) 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() result = model(input_ids, input_mask) result = model(input_ids) self.parent.assertEqual(result.last_hidden_state.shape, (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() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) 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() result = model( input_ids, attention_mask=input_mask, 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_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() result = model(input_ids, attention_mask=input_mask, labels=sequence_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) 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() result = model(input_ids, attention_mask=input_mask, labels=token_labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) def create_and_check_distilbert_for_multiple_choice( self, config, input_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = DistilBertForMultipleChoice(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_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, 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, 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 @require_torch class DistilBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( DistilBertModel, DistilBertForMaskedLM, DistilBertForMultipleChoice, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, DistilBertForTokenClassification, ) if is_torch_available() else None ) pipeline_model_mapping = ( { "feature-extraction": DistilBertModel, "fill-mask": DistilBertForMaskedLM, "question-answering": DistilBertForQuestionAnswering, "text-classification": DistilBertForSequenceClassification, "token-classification": DistilBertForTokenClassification, "zero-shot": DistilBertForSequenceClassification, } if is_torch_available() else {} ) fx_compatible = True test_pruning = True test_resize_embeddings = True test_resize_position_embeddings = True def setUp(self): self.model_tester = 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) def test_for_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_distilbert_for_multiple_choice(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = DistilBertModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow @require_torch_gpu def test_torchscript_device_change(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # BertForMultipleChoice behaves incorrectly in JIT environments. if model_class == DistilBertForMultipleChoice: return config.torchscript = True model = model_class(config=config) inputs_dict = self._prepare_for_class(inputs_dict, model_class) traced_model = torch.jit.trace( model, (inputs_dict["input_ids"].to("cpu"), inputs_dict["attention_mask"].to("cpu")) ) with tempfile.TemporaryDirectory() as tmp: torch.jit.save(traced_model, os.path.join(tmp, "traced_model.pt")) loaded = torch.jit.load(os.path.join(tmp, "traced_model.pt"), map_location=torch_device) loaded(inputs_dict["input_ids"].to(torch_device), inputs_dict["attention_mask"].to(torch_device)) @require_torch class DistilBertModelIntergrationTest(unittest.TestCase): @slow def test_inference_no_head_absolute_embedding(self): model = DistilBertModel.from_pretrained("distilbert-base-uncased") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 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] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] ) self.assertTrue(torch.allclose(output[:, 1:4, 1:4], expected_slice, atol=1e-4))