# 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 unittest from transformers import FlaubertConfig, is_sacremoses_available, is_torch_available from transformers.testing_utils import require_torch, 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 ( FlaubertForMultipleChoice, FlaubertForQuestionAnswering, FlaubertForQuestionAnsweringSimple, FlaubertForSequenceClassification, FlaubertForTokenClassification, FlaubertModel, FlaubertWithLMHeadModel, ) from transformers.models.flaubert.modeling_flaubert import create_sinusoidal_embeddings class FlaubertModelTester: def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_lengths=True, use_token_type_ids=True, use_labels=True, gelu_activation=True, sinusoidal_embeddings=False, causal=False, asm=False, n_langs=2, vocab_size=99, n_special=0, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=12, type_sequence_label_size=2, initializer_range=0.02, num_labels=3, num_choices=4, summary_type="last", use_proj=None, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training self.use_input_lengths = use_input_lengths self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.gelu_activation = gelu_activation self.sinusoidal_embeddings = sinusoidal_embeddings self.causal = causal self.asm = asm self.n_langs = n_langs self.vocab_size = vocab_size self.n_special = n_special self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads 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.summary_type = summary_type self.use_proj = use_proj self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = random_attention_mask([self.batch_size, self.seq_length]) input_lengths = None if self.use_input_lengths: input_lengths = ( ids_tensor([self.batch_size], vocab_size=2) + self.seq_length - 2 ) # small variation of seq_length token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.n_langs) sequence_labels = None token_labels = None is_impossible_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) is_impossible_labels = ids_tensor([self.batch_size], 2).float() choice_labels = ids_tensor([self.batch_size], self.num_choices) config = self.get_config() return ( config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) def get_config(self): return FlaubertConfig( vocab_size=self.vocab_size, n_special=self.n_special, emb_dim=self.hidden_size, n_layers=self.num_hidden_layers, n_heads=self.num_attention_heads, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, gelu_activation=self.gelu_activation, sinusoidal_embeddings=self.sinusoidal_embeddings, asm=self.asm, causal=self.causal, n_langs=self.n_langs, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, summary_type=self.summary_type, use_proj=self.use_proj, ) def create_and_check_flaubert_model( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = FlaubertModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, lengths=input_lengths, langs=token_type_ids) result = model(input_ids, langs=token_type_ids) 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_flaubert_lm_head( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = FlaubertWithLMHeadModel(config) model.to(torch_device) model.eval() result = model(input_ids, token_type_ids=token_type_ids, labels=token_labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def create_and_check_flaubert_simple_qa( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = FlaubertForQuestionAnsweringSimple(config) model.to(torch_device) model.eval() result = model(input_ids) result = model(input_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_flaubert_qa( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = FlaubertForQuestionAnswering(config) model.to(torch_device) model.eval() result = model(input_ids) result_with_labels = model( input_ids, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, p_mask=input_mask, ) result_with_labels = model( input_ids, start_positions=sequence_labels, end_positions=sequence_labels, cls_index=sequence_labels, is_impossible=is_impossible_labels, ) (total_loss,) = result_with_labels.to_tuple() result_with_labels = model(input_ids, start_positions=sequence_labels, end_positions=sequence_labels) (total_loss,) = result_with_labels.to_tuple() self.parent.assertEqual(result_with_labels.loss.shape, ()) self.parent.assertEqual(result.start_top_log_probs.shape, (self.batch_size, model.config.start_n_top)) self.parent.assertEqual(result.start_top_index.shape, (self.batch_size, model.config.start_n_top)) self.parent.assertEqual( result.end_top_log_probs.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual( result.end_top_index.shape, (self.batch_size, model.config.start_n_top * model.config.end_n_top) ) self.parent.assertEqual(result.cls_logits.shape, (self.batch_size,)) def create_and_check_flaubert_sequence_classif( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): model = FlaubertForSequenceClassification(config) model.to(torch_device) model.eval() result = model(input_ids) result = model(input_ids, labels=sequence_labels) self.parent.assertEqual(result.loss.shape, ()) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def create_and_check_flaubert_token_classif( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): config.num_labels = self.num_labels model = FlaubertForTokenClassification(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_flaubert_multiple_choice( self, config, input_ids, token_type_ids, input_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ): config.num_choices = self.num_choices model = FlaubertForMultipleChoice(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_lengths, sequence_labels, token_labels, is_impossible_labels, choice_labels, input_mask, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "token_type_ids": token_type_ids, "lengths": input_lengths, "attention_mask": input_mask, } return config, inputs_dict @require_torch class FlaubertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( FlaubertModel, FlaubertWithLMHeadModel, FlaubertForQuestionAnswering, FlaubertForQuestionAnsweringSimple, FlaubertForSequenceClassification, FlaubertForTokenClassification, FlaubertForMultipleChoice, ) if is_torch_available() else () ) # Doesn't run generation tests. Outdated custom `prepare_inputs_for_generation` -- TODO @gante all_generative_model_classes = () pipeline_model_mapping = ( { "feature-extraction": FlaubertModel, "fill-mask": FlaubertWithLMHeadModel, "question-answering": FlaubertForQuestionAnsweringSimple, "text-classification": FlaubertForSequenceClassification, "token-classification": FlaubertForTokenClassification, "zero-shot": FlaubertForSequenceClassification, } if is_torch_available() and is_sacremoses_available() else {} ) # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): if ( pipeline_test_case_name == "QAPipelineTests" and tokenizer_name is not None and not tokenizer_name.endswith("Fast") ): # `QAPipelineTests` fails for a few models when the slower tokenizer are used. # (The slower tokenizers were never used for pipeline tests before the pipeline testing rework) # TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer return True return False # Flaubert has 2 QA models -> need to manually set the correct labels for one of them here def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class.__name__ == "FlaubertForQuestionAnswering": inputs_dict["start_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) inputs_dict["end_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = FlaubertModelTester(self) self.config_tester = ConfigTester(self, config_class=FlaubertConfig, emb_dim=37) def test_config(self): self.config_tester.run_common_tests() def test_flaubert_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_model(*config_and_inputs) # Copied from tests/models/distilbert/test_modeling_distilbert.py with Distilbert->Flaubert def test_flaubert_model_with_sinusoidal_encodings(self): config = FlaubertConfig(sinusoidal_embeddings=True) model = FlaubertModel(config=config) sinusoidal_pos_embds = torch.empty((config.max_position_embeddings, config.emb_dim), dtype=torch.float32) create_sinusoidal_embeddings(config.max_position_embeddings, config.emb_dim, sinusoidal_pos_embds) self.model_tester.parent.assertTrue(torch.equal(model.position_embeddings.weight, sinusoidal_pos_embds)) def test_flaubert_lm_head(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_lm_head(*config_and_inputs) def test_flaubert_simple_qa(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_simple_qa(*config_and_inputs) def test_flaubert_qa(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_qa(*config_and_inputs) def test_flaubert_sequence_classif(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_sequence_classif(*config_and_inputs) def test_flaubert_token_classif(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_token_classif(*config_and_inputs) def test_flaubert_multiple_choice(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_flaubert_multiple_choice(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "flaubert/flaubert_small_cased" model = FlaubertModel.from_pretrained(model_name) self.assertIsNotNone(model) @require_torch class FlaubertModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head_absolute_embedding(self): model = FlaubertModel.from_pretrained("flaubert/flaubert_base_cased") input_ids = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]]) with torch.no_grad(): output = model(input_ids)[0] expected_shape = torch.Size((1, 11, 768)) self.assertEqual(output.shape, expected_shape) expected_slice = torch.tensor( [[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] ) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)