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| # coding=utf-8 | |
| # 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_tf_available, 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, | |
| ) | |
| from transformers.models.lxmert.modeling_lxmert import LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST | |
| if is_tf_available(): | |
| import tensorflow as tf | |
| 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, | |
| layer_norm_eps=1e-12, | |
| pad_token_id=0, | |
| num_qa_labels=30, | |
| num_object_labels=16, | |
| num_attr_labels=4, | |
| num_visual_features=10, | |
| l_layers=2, | |
| 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.layer_norm_eps = layer_norm_eps | |
| 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, | |
| layer_norm_eps=self.layer_norm_eps, | |
| 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 | |
| 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 {} | |
| ) | |
| fx_compatible = True | |
| test_head_masking = False | |
| test_pruning = False | |
| test_torchscript = False | |
| # 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) | |
| def test_model_from_pretrained(self): | |
| for model_name in LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: | |
| 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) | |
| def prepare_tf_inputs_from_pt_inputs(self, pt_inputs_dict): | |
| tf_inputs_dict = {} | |
| for key, value in pt_inputs_dict.items(): | |
| # skip key that does not exist in tf | |
| if isinstance(value, dict): | |
| tf_inputs_dict[key] = self.prepare_pt_inputs_from_tf_inputs(value) | |
| elif isinstance(value, (list, tuple)): | |
| tf_inputs_dict[key] = (self.prepare_pt_inputs_from_tf_inputs(iter_value) for iter_value in value) | |
| elif type(value) == bool: | |
| tf_inputs_dict[key] = value | |
| elif key == "input_values": | |
| tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32) | |
| elif key == "pixel_values": | |
| tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32) | |
| elif key == "input_features": | |
| tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32) | |
| # other general float inputs | |
| elif value.is_floating_point(): | |
| tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32) | |
| else: | |
| tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.int32) | |
| return tf_inputs_dict | |
| class LxmertModelIntegrationTest(unittest.TestCase): | |
| def test_inference_no_head_absolute_embedding(self): | |
| model = LxmertModel.from_pretrained(LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) | |
| 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]]] | |
| ) | |
| self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4)) | |