# Copyright 2018 LXMERT Authors, The Hugging Face Team. # # 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 copy import unittest import numpy as np from transformers import LxmertConfig, is_torch_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_FOR_PRETRAINING_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, LxmertForPreTraining, LxmertForQuestionAnswering, LxmertModel, ) class LxmertModelTester: def __init__( self, parent, vocab_size=300, hidden_size=28, num_attention_heads=2, num_labels=2, intermediate_size=64, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, pad_token_id=0, num_qa_labels=30, num_object_labels=16, num_attr_labels=4, num_visual_features=10, l_layers=1, x_layers=1, r_layers=1, visual_feat_dim=128, visual_pos_dim=4, visual_loss_normalizer=6.67, seq_length=20, batch_size=4, is_training=True, task_matched=True, task_mask_lm=True, task_obj_predict=True, task_qa=True, visual_obj_loss=True, visual_attr_loss=True, visual_feat_loss=True, use_token_type_ids=True, use_lang_mask=True, output_attentions=False, output_hidden_states=False, scope=None, ): self.parent = parent self.vocab_size = vocab_size self.hidden_size = hidden_size self.num_attention_heads = num_attention_heads self.num_labels = num_labels 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.initializer_range = initializer_range self.pad_token_id = pad_token_id self.num_qa_labels = num_qa_labels self.num_object_labels = num_object_labels self.num_attr_labels = num_attr_labels self.l_layers = l_layers self.x_layers = x_layers self.r_layers = r_layers self.visual_feat_dim = visual_feat_dim self.visual_pos_dim = visual_pos_dim self.visual_loss_normalizer = visual_loss_normalizer self.seq_length = seq_length self.batch_size = batch_size self.is_training = is_training self.use_lang_mask = use_lang_mask self.task_matched = task_matched self.task_mask_lm = task_mask_lm self.task_obj_predict = task_obj_predict self.task_qa = task_qa self.visual_obj_loss = visual_obj_loss self.visual_attr_loss = visual_attr_loss self.visual_feat_loss = visual_feat_loss self.num_visual_features = num_visual_features self.use_token_type_ids = use_token_type_ids self.output_attentions = output_attentions self.output_hidden_states = output_hidden_states self.scope = scope self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers} def prepare_config_and_inputs(self): output_attentions = self.output_attentions input_ids = ids_tensor([self.batch_size, self.seq_length], vocab_size=self.vocab_size) visual_feats = torch.rand(self.batch_size, self.num_visual_features, self.visual_feat_dim, device=torch_device) bounding_boxes = torch.rand(self.batch_size, self.num_visual_features, 4, device=torch_device) input_mask = None if self.use_lang_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) obj_labels = None if self.task_obj_predict: obj_labels = {} if self.visual_attr_loss and self.task_obj_predict: obj_labels["attr"] = ( ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels), ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels), ) if self.visual_feat_loss and self.task_obj_predict: obj_labels["feat"] = ( ids_tensor( [self.batch_size, self.num_visual_features, self.visual_feat_dim], self.num_visual_features ), ids_tensor([self.batch_size, self.num_visual_features], self.num_visual_features), ) if self.visual_obj_loss and self.task_obj_predict: obj_labels["obj"] = ( ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels), ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels), ) ans = None if self.task_qa: ans = ids_tensor([self.batch_size], self.num_qa_labels) masked_lm_labels = None if self.task_mask_lm: masked_lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) matched_label = None if self.task_matched: matched_label = ids_tensor([self.batch_size], self.num_labels) config = self.get_config() return ( config, input_ids, visual_feats, bounding_boxes, token_type_ids, input_mask, obj_labels, masked_lm_labels, matched_label, ans, output_attentions, ) def get_config(self): return LxmertConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_attention_heads=self.num_attention_heads, num_labels=self.num_labels, 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, pad_token_id=self.pad_token_id, num_qa_labels=self.num_qa_labels, num_object_labels=self.num_object_labels, num_attr_labels=self.num_attr_labels, l_layers=self.l_layers, x_layers=self.x_layers, r_layers=self.r_layers, visual_feat_dim=self.visual_feat_dim, visual_pos_dim=self.visual_pos_dim, visual_loss_normalizer=self.visual_loss_normalizer, task_matched=self.task_matched, task_mask_lm=self.task_mask_lm, task_obj_predict=self.task_obj_predict, task_qa=self.task_qa, visual_obj_loss=self.visual_obj_loss, visual_attr_loss=self.visual_attr_loss, visual_feat_loss=self.visual_feat_loss, output_attentions=self.output_attentions, output_hidden_states=self.output_hidden_states, ) def create_and_check_lxmert_model( self, config, input_ids, visual_feats, bounding_boxes, token_type_ids, input_mask, obj_labels, masked_lm_labels, matched_label, ans, output_attentions, ): model = LxmertModel(config=config) model.to(torch_device) model.eval() result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, output_attentions=output_attentions, ) result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, output_attentions=not output_attentions, ) result = model(input_ids, visual_feats, bounding_boxes, return_dict=False) result = model(input_ids, visual_feats, bounding_boxes, return_dict=True) self.parent.assertEqual(result.language_output.shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual( result.vision_output.shape, (self.batch_size, self.num_visual_features, self.hidden_size) ) self.parent.assertEqual(result.pooled_output.shape, (self.batch_size, self.hidden_size)) def create_and_check_lxmert_for_question_answering( self, config, input_ids, visual_feats, bounding_boxes, token_type_ids, input_mask, obj_labels, masked_lm_labels, matched_label, ans, output_attentions, ): model = LxmertForQuestionAnswering(config=config) model.to(torch_device) model.eval() result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, labels=ans, output_attentions=output_attentions, ) result = model(input_ids, visual_feats, bounding_boxes, labels=ans) result = model( input_ids, visual_feats, bounding_boxes, labels=ans, token_type_ids=token_type_ids, attention_mask=input_mask, output_attentions=output_attentions, ) result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, labels=ans, output_attentions=not output_attentions, ) self.parent.assertEqual(result.question_answering_score.shape, (self.batch_size, self.num_qa_labels)) def create_and_check_lxmert_for_pretraining( self, config, input_ids, visual_feats, bounding_boxes, token_type_ids, input_mask, obj_labels, masked_lm_labels, matched_label, ans, output_attentions, ): model = LxmertForPreTraining(config=config) model.to(torch_device) model.eval() result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, masked_lm_labels=masked_lm_labels, obj_labels=obj_labels, matched_label=matched_label, ans=ans, output_attentions=output_attentions, ) result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, masked_lm_labels=masked_lm_labels, output_attentions=not output_attentions, return_dict=False, ) result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, masked_lm_labels=masked_lm_labels, ) result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, obj_labels=obj_labels, ) result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, matched_label=matched_label, ) result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, ans=ans, ) result = model( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, masked_lm_labels=masked_lm_labels, obj_labels=obj_labels, matched_label=matched_label, ans=ans, output_attentions=not output_attentions, ) self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size)) def resize_lxmert_num_qa_labels( self, config, input_ids, visual_feats, bounding_boxes, token_type_ids, input_mask, obj_labels, masked_lm_labels, matched_label, ans, output_attentions, ): start_labels = config.num_qa_labels num_large_labels = config.num_qa_labels * 2 num_small_labels = int(config.num_qa_labels * 2) less_labels_ans = ids_tensor([self.batch_size], num_small_labels) more_labels_ans = ids_tensor([self.batch_size], num_large_labels) model_pretrain = LxmertForPreTraining(config=config).to(torch_device) model_qa = LxmertForQuestionAnswering(config=config).to(torch_device) config.num_labels = num_small_labels end_labels = config.num_labels result_pretrain = model_pretrain( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, ans=ans, ) result_qa = model_qa( input_ids, visual_feats, bounding_boxes, labels=ans, token_type_ids=token_type_ids, attention_mask=input_mask, ) model_pretrain.resize_num_qa_labels(num_small_labels) model_qa.resize_num_qa_labels(num_small_labels) result_pretrain_less = model_pretrain( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, ans=less_labels_ans, ) result_qa_less = model_qa( input_ids, visual_feats, bounding_boxes, labels=less_labels_ans, token_type_ids=token_type_ids, attention_mask=input_mask, ) model_pretrain.resize_num_qa_labels(num_large_labels) model_qa.resize_num_qa_labels(num_large_labels) result_pretrain_more = model_pretrain( input_ids, visual_feats, bounding_boxes, token_type_ids=token_type_ids, attention_mask=input_mask, ans=more_labels_ans, ) result_qa_more = model_qa( input_ids, visual_feats, bounding_boxes, labels=more_labels_ans, token_type_ids=token_type_ids, attention_mask=input_mask, ) model_qa_labels = model_qa.num_qa_labels self.parent.assertNotEqual(start_labels, end_labels) self.parent.assertNotEqual(model_qa_labels, start_labels) self.parent.assertEqual(result_qa.question_answering_score.shape, (self.batch_size, start_labels)) self.parent.assertEqual(result_pretrain.question_answering_score.shape, (self.batch_size, start_labels)) self.parent.assertEqual(result_qa_less.question_answering_score.shape, (self.batch_size, num_small_labels)) self.parent.assertEqual( result_pretrain_less.question_answering_score.shape, (self.batch_size, num_small_labels) ) self.parent.assertEqual(result_qa_more.question_answering_score.shape, (self.batch_size, num_large_labels)) self.parent.assertEqual( result_pretrain_more.question_answering_score.shape, (self.batch_size, num_large_labels) ) def prepare_config_and_inputs_for_common(self, return_obj_labels=False): config_and_inputs = self.prepare_config_and_inputs() ( config, input_ids, visual_feats, bounding_boxes, token_type_ids, input_mask, obj_labels, masked_lm_labels, matched_label, ans, output_attentions, ) = config_and_inputs inputs_dict = { "input_ids": input_ids, "visual_feats": visual_feats, "visual_pos": bounding_boxes, "token_type_ids": token_type_ids, "attention_mask": input_mask, } if return_obj_labels: inputs_dict["obj_labels"] = obj_labels else: config.task_obj_predict = False return config, inputs_dict @require_torch class LxmertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = (LxmertModel, LxmertForPreTraining, LxmertForQuestionAnswering) if is_torch_available() else () pipeline_model_mapping = ( {"feature-extraction": LxmertModel, "question-answering": LxmertForQuestionAnswering} if is_torch_available() else {} ) # overwrite function because qa models takes different input label shape def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if return_labels: if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING): inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING): # special case for models like BERT that use multi-loss training for PreTraining inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device ) return inputs_dict def setUp(self): self.model_tester = LxmertModelTester(self) self.config_tester = ConfigTester(self, config_class=LxmertConfig, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() def test_lxmert_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lxmert_model(*config_and_inputs) def test_lxmert_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lxmert_for_question_answering(*config_and_inputs) def test_lxmert_pretraining(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_lxmert_for_pretraining(*config_and_inputs) def test_lxmert_question_answering_labels_resize(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.resize_lxmert_num_qa_labels(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "unc-nlp/lxmert-base-uncased" model = LxmertModel.from_pretrained(model_name) model.to(torch_device) self.assertIsNotNone(model) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() seq_len = getattr(self.model_tester, "seq_length", None) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1]) self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"]) self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"]) self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"]) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1]) self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"]) self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"]) self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"]) attentions = [language_attentions, vision_attentions, cross_encoder_attentions] attention_shapes = [ [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], [ self.model_tester.num_attention_heads, self.model_tester.num_visual_features, self.model_tester.num_visual_features, ], [self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features], ] for attention, attention_shape in zip(attentions, attention_shapes): self.assertListEqual(list(attention[0].shape[-3:]), attention_shape) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) # 2 hidden states were added self.assertEqual(out_len + 2, len(outputs)) language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1]) self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"]) self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"]) self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"]) attentions = [language_attentions, vision_attentions, cross_encoder_attentions] attention_shapes = [ [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], [ self.model_tester.num_attention_heads, self.model_tester.num_visual_features, self.model_tester.num_visual_features, ], [self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features], ] for attention, attention_shape in zip(attentions, attention_shapes): self.assertListEqual(list(attention[0].shape[-3:]), attention_shape) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) language_hidden_states, vision_hidden_states = outputs[-2], outputs[-1] self.assertEqual(len(language_hidden_states), self.model_tester.num_hidden_layers["language"] + 1) self.assertEqual(len(vision_hidden_states), self.model_tester.num_hidden_layers["vision"] + 1) seq_length = self.model_tester.seq_length num_visual_features = self.model_tester.num_visual_features self.assertListEqual( list(language_hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) self.assertListEqual( list(vision_hidden_states[0].shape[-2:]), [num_visual_features, self.model_tester.hidden_size], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) def test_retain_grad_hidden_states_attentions(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.output_hidden_states = True config.output_attentions = True # no need to test all models as different heads yield the same functionality model_class = self.all_model_classes[0] model = model_class(config) model.to(torch_device) inputs = self._prepare_for_class(inputs_dict, model_class) outputs = model(**inputs) hidden_states_lang = outputs.language_hidden_states[0] attentions_lang = outputs.language_attentions[0] hidden_states_vision = outputs.vision_hidden_states[0] attentions_vision = outputs.vision_attentions[0] hidden_states_lang.retain_grad() attentions_lang.retain_grad() hidden_states_vision.retain_grad() attentions_vision.retain_grad() outputs.language_output.flatten()[0].backward(retain_graph=True) outputs.vision_output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states_lang.grad) self.assertIsNotNone(attentions_vision.grad) self.assertIsNotNone(hidden_states_vision.grad) self.assertIsNotNone(attentions_vision.grad) @unittest.skip( reason="This architecture has tied weights by default and there is no way to remove it, check: https://github.com/huggingface/transformers/pull/31771#issuecomment-2210915245" ) def test_load_save_without_tied_weights(self): pass @require_torch class LxmertModelIntegrationTest(unittest.TestCase): @slow def test_inference_no_head_absolute_embedding(self): model = LxmertModel.from_pretrained("unc-nlp/lxmert-base-uncased") input_ids = torch.tensor([[101, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 102]]) num_visual_features = 10 _, visual_feats = np.random.seed(0), np.random.rand(1, num_visual_features, model.config.visual_feat_dim) _, visual_pos = np.random.seed(0), np.random.rand(1, num_visual_features, 4) visual_feats = torch.as_tensor(visual_feats, dtype=torch.float32) visual_pos = torch.as_tensor(visual_pos, dtype=torch.float32) output = model(input_ids, visual_feats=visual_feats, visual_pos=visual_pos)[0] expected_shape = torch.Size([1, 11, 768]) self.assertEqual(expected_shape, output.shape) expected_slice = torch.tensor( [[[0.2417, -0.9807, 0.1480], [1.2541, -0.8320, 0.5112], [1.4070, -1.1052, 0.6990]]] ) torch.testing.assert_close(output[:, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)