# 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, slow, torch_device if is_torch_available(): import torch from transformers import ( LongformerConfig, LongformerModel, LongformerForMaskedLM, LongformerForSequenceClassification, LongformerForTokenClassification, LongformerForQuestionAnswering, LongformerForMultipleChoice, ) class LongformerModelTester(object): 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=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, attention_window=4, ): 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 self.attention_window = attention_window # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size # [num_attention_heads, encoder_seq_length, encoder_key_length], but LongformerSelfAttention # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1] # because its local attention only attends to `self.attention_window + 1` locations self.key_length = self.attention_window + 1 # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for # the `test_attention_outputs` and `test_hidden_states_output` tests self.encoder_seq_length = ( self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window ) 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 = LongformerConfig( 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, attention_window=self.attention_window, ) return config, input_ids, token_type_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_longformer_model( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LongformerModel(config=config) model.to(torch_device) model.eval() sequence_output, pooled_output = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids) sequence_output, pooled_output = model(input_ids, token_type_ids=token_type_ids) sequence_output, pooled_output = model(input_ids) result = { "sequence_output": sequence_output, "pooled_output": pooled_output, } self.parent.assertListEqual( list(result["sequence_output"].size()), [self.batch_size, self.seq_length, self.hidden_size] ) self.parent.assertListEqual(list(result["pooled_output"].size()), [self.batch_size, self.hidden_size]) def create_and_check_longformer_for_masked_lm( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LongformerForMaskedLM(config=config) model.to(torch_device) model.eval() loss, prediction_scores = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, 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_longformer_for_question_answering( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): model = LongformerForQuestionAnswering(config=config) model.to(torch_device) model.eval() loss, start_logits, end_logits = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, 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_longformer_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 = LongformerForSequenceClassification(config) model.to(torch_device) model.eval() loss, logits = model( input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, 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_longformer_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 = LongformerForTokenClassification(config=config) model.to(torch_device) model.eval() loss, logits = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, 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 create_and_check_longformer_for_multiple_choice( self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels ): config.num_choices = self.num_choices model = LongformerForMultipleChoice(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() loss, logits = model( multiple_choice_inputs_ids, attention_mask=multiple_choice_input_mask, token_type_ids=multiple_choice_token_type_ids, labels=choice_labels, ) result = { "loss": loss, "logits": logits, } self.parent.assertListEqual(list(result["logits"].size()), [self.batch_size, self.num_choices]) self.check_loss_output(result) 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 def prepare_config_and_inputs_for_question_answering(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 # Replace sep_token_id by some random id input_ids[input_ids == config.sep_token_id] = torch.randint(0, config.vocab_size, (1,)).item() # Make sure there are exactly three sep_token_id input_ids[:, -3:] = config.sep_token_id input_mask = torch.ones_like(input_ids) return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels @require_torch class LongformerModelTest(ModelTesterMixin, unittest.TestCase): test_pruning = False # pruning is not supported test_headmasking = False # head masking is not supported test_torchscript = False all_model_classes = (LongformerForMaskedLM, LongformerModel) if is_torch_available() else () def setUp(self): self.model_tester = LongformerModelTester(self) self.config_tester = ConfigTester(self, config_class=LongformerConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_longformer_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_longformer_model(*config_and_inputs) def test_longformer_for_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_longformer_for_masked_lm(*config_and_inputs) def test_longformer_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs_for_question_answering() self.model_tester.create_and_check_longformer_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_longformer_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_longformer_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_longformer_for_multiple_choice(*config_and_inputs) class LongformerModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head(self): model = LongformerModel.from_pretrained("longformer-base-4096") model.to(torch_device) # 'Hello world! ' repeated 1000 times input_ids = torch.tensor( [[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device ) # long input attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=input_ids.device) attention_mask[:, [1, 4, 21]] = 2 # Set global attention on a few random positions output = model(input_ids, attention_mask=attention_mask)[0] expected_output_sum = torch.tensor(74585.8594, device=torch_device) expected_output_mean = torch.tensor(0.0243, device=torch_device) self.assertTrue(torch.allclose(output.sum(), expected_output_sum, atol=1e-4)) self.assertTrue(torch.allclose(output.mean(), expected_output_mean, atol=1e-4)) @slow def test_inference_masked_lm(self): model = LongformerForMaskedLM.from_pretrained("longformer-base-4096") model.to(torch_device) # 'Hello world! ' repeated 1000 times input_ids = torch.tensor( [[0] + [20920, 232, 328, 1437] * 1000 + [2]], dtype=torch.long, device=torch_device ) # long input loss, prediction_scores = model(input_ids, masked_lm_labels=input_ids) expected_loss = torch.tensor(0.0620, device=torch_device) expected_prediction_scores_sum = torch.tensor(-6.1599e08, device=torch_device) expected_prediction_scores_mean = torch.tensor(-3.0622, device=torch_device) input_ids = input_ids.to(torch_device) self.assertTrue(torch.allclose(loss, expected_loss, atol=1e-4)) self.assertTrue(torch.allclose(prediction_scores.sum(), expected_prediction_scores_sum, atol=1e-4)) self.assertTrue(torch.allclose(prediction_scores.mean(), expected_prediction_scores_mean, atol=1e-4))