# 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 tempfile import unittest import pytest from transformers import DistilBertConfig, is_torch_available from transformers.testing_utils import require_flash_attn, require_torch, require_torch_accelerator, 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 ( AutoTokenizer, DistilBertForMaskedLM, DistilBertForMultipleChoice, DistilBertForQuestionAnswering, DistilBertForSequenceClassification, DistilBertForTokenClassification, DistilBertModel, ) from transformers.models.distilbert.modeling_distilbert import _create_sinusoidal_embeddings from transformers.pytorch_utils import is_torch_greater_or_equal_than_2_4 class DistilBertModelTester: 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=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, 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 {} ) 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_distilbert_model_with_sinusoidal_encodings(self): config = DistilBertConfig(sinusoidal_pos_embds=True) model = DistilBertModel(config=config) sinusoidal_pos_embds = torch.empty((config.max_position_embeddings, config.dim), dtype=torch.float32) _create_sinusoidal_embeddings(config.max_position_embeddings, config.dim, sinusoidal_pos_embds) self.model_tester.parent.assertTrue( torch.equal(model.embeddings.position_embeddings.weight, sinusoidal_pos_embds) ) 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): model_name = "distilbert-base-uncased" model = DistilBertModel.from_pretrained(model_name) self.assertIsNotNone(model) # Because DistilBertForMultipleChoice requires inputs with different shapes we need to override this test. @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence(self): import torch for model_class in self.all_model_classes: dummy_input = torch.LongTensor( [ [1, 2, 3, 4], [1, 2, 8, 9], [1, 2, 11, 12], [1, 2, 13, 14], ] ).to(torch_device) dummy_attention_mask = torch.LongTensor( [ [0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], ] ).to(torch_device) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained(tmpdirname, dtype=torch.bfloat16) model.to(torch_device) logits = model(dummy_input, output_hidden_states=True).hidden_states[-1] logits_fa = model_fa(dummy_input, output_hidden_states=True).hidden_states[-1] torch.testing.assert_close(logits_fa, logits, rtol=4e-2, atol=4e-2) output_fa = model_fa(dummy_input, attention_mask=dummy_attention_mask, output_hidden_states=True) logits_fa = output_fa.hidden_states[-1] output = model(dummy_input, attention_mask=dummy_attention_mask, output_hidden_states=True) logits = output.hidden_states[-1] torch.testing.assert_close(logits_fa[1:], logits[1:], rtol=4e-2, atol=4e-2) # Because DistilBertForMultipleChoice requires inputs with different shapes we need to override this test. @require_flash_attn @require_torch_accelerator @pytest.mark.flash_attn_test @slow def test_flash_attn_2_inference_equivalence_right_padding(self): import torch for model_class in self.all_model_classes: dummy_input = torch.LongTensor( [ [1, 2, 3, 4], [1, 2, 8, 9], [1, 2, 11, 12], [1, 2, 13, 14], ] ).to(torch_device) dummy_attention_mask = torch.LongTensor( [ [0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], [0, 1, 1, 1], ] ).to(torch_device) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_fa = model_class.from_pretrained( tmpdirname, dtype=torch.bfloat16, attn_implementation="flash_attention_2" ) model_fa.to(torch_device) model = model_class.from_pretrained( tmpdirname, dtype=torch.bfloat16, ) model.to(torch_device) logits = model(dummy_input, output_hidden_states=True).hidden_states[-1] logits_fa = model_fa(dummy_input, output_hidden_states=True).hidden_states[-1] torch.testing.assert_close(logits_fa, logits, rtol=4e-2, atol=4e-2) output_fa = model_fa(dummy_input, attention_mask=dummy_attention_mask, output_hidden_states=True) logits_fa = output_fa.hidden_states[-1] output = model(dummy_input, attention_mask=dummy_attention_mask, output_hidden_states=True) logits = output.hidden_states[-1] torch.testing.assert_close(logits_fa[:-1], logits[:-1], rtol=4e-2, atol=4e-2) @require_torch class DistilBertModelIntegrationTest(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]]] ) torch.testing.assert_close(output[:, 1:4, 1:4], expected_slice, rtol=1e-4, atol=1e-4) @pytest.mark.torch_export_test @slow def test_export(self): if not is_torch_greater_or_equal_than_2_4: self.skipTest(reason="This test requires torch >= 2.4 to run.") distilbert_model = "distilbert-base-uncased" device = "cpu" attn_implementation = "sdpa" max_length = 64 tokenizer = AutoTokenizer.from_pretrained(distilbert_model) inputs = tokenizer( f"Paris is the {tokenizer.mask_token} of France.", return_tensors="pt", padding="max_length", max_length=max_length, ) model = DistilBertForMaskedLM.from_pretrained( distilbert_model, device_map=device, attn_implementation=attn_implementation, ) logits = model(**inputs).logits eager_predicted_mask = tokenizer.decode(logits[0, 4].topk(5).indices) self.assertEqual( eager_predicted_mask.split(), ["capital", "birthplace", "northernmost", "centre", "southernmost"], ) exported_program = torch.export.export( model, args=(inputs["input_ids"],), kwargs={"attention_mask": inputs["attention_mask"]}, strict=True, ) result = exported_program.module().forward(inputs["input_ids"], inputs["attention_mask"]) exported_predicted_mask = tokenizer.decode(result.logits[0, 4].topk(5).indices) self.assertEqual(eager_predicted_mask, exported_predicted_mask)