# coding=utf-8 # Copyright 2023 The Intel Labs Team Authors, The Microsoft Research Team Authors and HuggingFace Inc. 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. """ Testing suite for the PyTorch BridgeTower model. """ import tempfile import unittest import numpy as np from transformers import BridgeTowerConfig, is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( BridgeTowerForContrastiveLearning, BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM, BridgeTowerModel, ) from transformers.models.bridgetower.modeling_bridgetower import BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_10 else: is_torch_greater_or_equal_than_1_10 = False if is_vision_available(): from PIL import Image from transformers import BridgeTowerProcessor class BridgeTowerModelTester: def __init__( self, parent, share_cross_modal_transformer_layers=True, drop_rate=0.1, head_hidden_scale=2, hidden_act="gelu", hidden_size=768, initializer_factor=1, is_encoder_decoder=False, layer_norm_eps=1e-05, share_link_tower_layers=False, link_tower_type="add", num_attention_heads=12, num_hidden_layers=6, tie_word_embeddings=False, init_layernorm_from_vision_encoder=False, output_hidden_states=False, text_config=None, vision_config=None, image_size=288, contrastive_hidden_size=512, logit_scale_init_value=2.6592, ): self.parent = parent self.share_cross_modal_transformer_layers = share_cross_modal_transformer_layers self.drop_rate = drop_rate self.head_hidden_scale = head_hidden_scale self.hidden_act = hidden_act self.hidden_size = hidden_size self.initializer_factor = initializer_factor self.is_encoder_decoder = is_encoder_decoder self.layer_norm_eps = layer_norm_eps self.share_link_tower_layers = share_link_tower_layers self.link_tower_type = link_tower_type self.num_attention_heads = num_attention_heads self.num_hidden_layers = num_hidden_layers self.tie_word_embeddings = tie_word_embeddings self.init_layernorm_from_vision_encoder = init_layernorm_from_vision_encoder self.vocab_size = 99 self.num_channels = 3 self.seq_length = 4 self.num_image_features = 325 self.batch_size = 1 self.image_size = image_size self.is_training = False self.expected_num_hidden_layers = 32 self.output_hidden_states = output_hidden_states self.contrastive_hidden_size = contrastive_hidden_size self.logit_scale_init_value = logit_scale_init_value def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = random_attention_mask([self.batch_size, self.seq_length]) pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) pixel_mask = random_attention_mask([self.batch_size, self.image_size, self.image_size]) config = self.get_config() return (config, input_ids, attention_mask, pixel_values, pixel_mask) def get_config(self): return BridgeTowerConfig( share_cross_modal_transformer_layers=self.share_cross_modal_transformer_layers, drop_rate=self.drop_rate, head_hidden_scale=self.head_hidden_scale, hidden_act=self.hidden_act, hidden_size=self.hidden_size, initializer_factor=self.initializer_factor, image_size=self.image_size, is_encoder_decoder=self.is_encoder_decoder, layer_norm_eps=self.layer_norm_eps, share_link_tower_layers=self.share_link_tower_layers, link_tower_type=self.link_tower_type, num_attention_heads=self.num_attention_heads, num_hidden_layers=self.num_hidden_layers, tie_word_embeddings=self.tie_word_embeddings, init_layernorm_from_vision_encoder=self.init_layernorm_from_vision_encoder, num_channels=self.num_channels, output_hidden_states=self.output_hidden_states, contrastive_hidden_size=self.contrastive_hidden_size, logit_scale_init_value=self.logit_scale_init_value, ) def create_and_check_model( self, config, input_ids, attention_mask, pixel_values, pixel_mask, ): model = BridgeTowerModel(config=config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values) self.parent.assertEqual(result["text_features"].shape, (self.batch_size, self.seq_length, self.hidden_size)) self.parent.assertEqual( result["image_features"].shape, (self.batch_size, self.num_image_features, self.hidden_size) ) self.parent.assertEqual(result["pooler_output"].shape, (self.batch_size, 2 * self.hidden_size)) def create_and_check_for_image_and_text_retrieval( self, config, input_ids, attention_mask, pixel_values, pixel_mask, ): bridgetower_itm_output_last_dimension = 2 model = BridgeTowerForImageAndTextRetrieval(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, bridgetower_itm_output_last_dimension)) def create_and_check_for_masked_language_modeling( self, config, input_ids, attention_mask, pixel_values, pixel_mask, ): model = BridgeTowerForMaskedLM(config) model.to(torch_device) model.eval() result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values, pixel_mask=pixel_mask) result = model(input_ids, attention_mask=attention_mask, pixel_values=pixel_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, 50265)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids, attention_mask, pixel_values, pixel_mask) = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, "pixel_mask": pixel_mask, } return config, inputs_dict @slow @require_torch @unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "BridgeTower is only available in torch v1.10+") class BridgeTowerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( BridgeTowerModel, BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM, BridgeTowerForContrastiveLearning, ) if is_torch_available() else () ) pipeline_model_mapping = {"feature-extraction": BridgeTowerModel} if is_torch_available() else {} is_training = False test_headmasking = False test_pruning = False test_torchscript = False test_resize_embeddings = False has_attentions = False # function to extract meaningful tensor from output per different model_class def extract_output(self, outputs, model_class): return outputs["pooler_output"] if model_class == "BridgeTowerModel" else outputs["logits"] def setUp(self): self.model_tester = BridgeTowerModelTester(self) self.config_tester = ConfigTester(self, config_class=BridgeTowerConfig, hidden_size=37, vocab_size=99) def test_config(self): self.config_tester.run_common_tests() def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_image_and_text_retrieval(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_and_text_retrieval(*config_and_inputs) def test_for_masked_language_modeling(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_language_modeling(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = BridgeTowerModel.from_pretrained(model_name) self.assertIsNotNone(model) @slow def test_save_load_fast_init_from_base(self): # Override as it is a slow test on this model super().test_save_load_fast_init_from_base() # Override as extracting meaningful tensor from output is different for BridgeTower def test_save_load(self): config, input_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**input_dict) out_2 = self.extract_output(outputs, model_class.__name__) out_2 = out_2.cpu().numpy() out_2[np.isnan(out_2)] = 0 with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model = model_class.from_pretrained(tmpdirname) model.to(torch_device) with torch.no_grad(): after_outputs = model(**input_dict) # Make sure we don't have nans out_1 = self.extract_output(after_outputs, model_class.__name__) out_1 = out_1.cpu().numpy() out_1[np.isnan(out_1)] = 0 max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) # Override this as `hidden states output` is different for BridgeTower 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)) hidden_states_text, hidden_states_vision, hidden_states_cross = ( outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states ) expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual( sum((len(hidden_states_text), len(hidden_states_vision), len(hidden_states_cross))), expected_num_layers, ) seq_length = self.model_tester.seq_length num_image_features = self.model_tester.num_image_features self.assertListEqual( list(hidden_states_text[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) self.assertListEqual( list(hidden_states_vision[0].shape), [num_image_features, 1, self.model_tester.hidden_size], ) self.assertListEqual( list(hidden_states_cross[0][0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) self.assertListEqual( list(hidden_states_cross[0][1].shape[-2:]), [num_image_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) # Override as `hidden states output` is different for BridgeTower 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 = self.has_attentions # 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) output = outputs[0] # Encoder-/Decoder-only models hidden_states = outputs.hidden_states[0][0] hidden_states.retain_grad() if self.has_attentions: attentions = outputs.attentions[0][0] attentions.retain_grad() output.flatten()[0].backward(retain_graph=True) self.assertIsNotNone(hidden_states.grad) if self.has_attentions: self.assertIsNotNone(attentions.grad) # override as the `logit_scale` parameter initilization is different for BRIDGE TOWER def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): if param.requires_grad: if name == "logit_scale": self.assertAlmostEqual( param.data.item(), config.logit_scale_init_value, delta=1e-3, msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) else: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) @unittest.skip(reason="""Bridge Tower does not have input/output embeddings. So this test is not applicable.""") def test_model_common_attributes(self): pass @unittest.skip(reason="""Bridge Tower does not have input/output embeddings. Thus this test is not applicable.""") def test_inputs_embeds(self): pass # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision @unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "BridgeTower is only available in torch v1.10+") class BridgeTowerModelIntegrationTest(unittest.TestCase): @cached_property def default_processor(self): return ( BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-base-itm-mlm") if is_vision_available() else None ) @slow def test_image_and_text_retrieval(self): model = BridgeTowerForImageAndTextRetrieval.from_pretrained("BridgeTower/bridgetower-base-itm-mlm").to( torch_device ) model.eval() processor = self.default_processor image = prepare_img() text = "a bunch of cats laying on a tower." inputs = processor(image, text, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size([1, 2]) self.assertEqual(outputs.logits.shape, expected_shape) self.assertTrue(outputs.logits[0, 1].item() > outputs.logits[0, 0].item()) # verify loss inputs["labels"] = torch.ones(1, dtype=torch.long, device=torch_device) inputs = inputs.to(torch_device) with torch.no_grad(): outputs = model(**inputs) self.assertAlmostEqual(outputs.loss.item(), 0.5108, places=4) @slow def test_masked_language_modeling(self): model = BridgeTowerForMaskedLM.from_pretrained("BridgeTower/bridgetower-base-itm-mlm").to(torch_device) model.eval() processor = self.default_processor image = prepare_img() text = "a bunch of laying on a tower." inputs = processor(image, text, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) # verify the logits expected_shape = torch.Size([1, 11, 50265]) self.assertEqual(outputs.logits.shape, expected_shape) # verify predicted word predicted_id = outputs.logits.argmax(dim=-1).squeeze(0).tolist()[4] self.assertTrue(processor.decode([predicted_id]) == " cats") # verify loss inputs["labels"] = inputs["input_ids"].clone() inputs = inputs.to(torch_device) with torch.no_grad(): outputs = model(**inputs) self.assertAlmostEqual(outputs.loss.item(), 5.7373, places=4) @slow def test_constrastive_learning(self): model = BridgeTowerForContrastiveLearning.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc").to( torch_device ) model.eval() processor = BridgeTowerProcessor.from_pretrained("BridgeTower/bridgetower-large-itm-mlm-itc") image = prepare_img() text = "a bunch of cats laying on a tower." inputs = processor(image, text, padding=True, return_tensors="pt").to(torch_device) with torch.no_grad(): outputs = model(**inputs, output_hidden_states=True, return_loss=True) # verify the logits expected_shape = torch.Size([1, 3, 512]) self.assertEqual(outputs.logits.shape, expected_shape) @slow @require_torch @unittest.skipIf(not is_torch_greater_or_equal_than_1_10, "BridgeTower is only available in torch v1.10+") class BridgeTowerModelTrainingTest(unittest.TestCase): all_training_supported_model_classes = ( (BridgeTowerForImageAndTextRetrieval, BridgeTowerForMaskedLM, BridgeTowerForContrastiveLearning) if is_torch_available() else () ) def setUp(self): self.model_tester = BridgeTowerModelTester(self) self.config_tester = ConfigTester(self, config_class=BridgeTowerConfig, hidden_size=37, vocab_size=99) def _prepare_inputs_for_training(self, model_class): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if model_class == BridgeTowerForMaskedLM: inputs_dict["labels"] = inputs_dict["input_ids"] elif model_class == BridgeTowerForImageAndTextRetrieval: inputs_dict["labels"] = ids_tensor([1], 2) elif model_class == BridgeTowerForContrastiveLearning: inputs_dict["return_loss"] = True return config, inputs_dict def _get_non_used_layer_names(self, model_class): non_used_layer_names = ["text_model.pooler"] if model_class == BridgeTowerForMaskedLM: non_used_layer_names = non_used_layer_names + [ "cross_modal_image_layers.5", "cross_modal_image_pooler", "cross_modal_text_pooler", ] return non_used_layer_names def _is_layer_used(self, model_class, layer_name): non_used_layer_names = self._get_non_used_layer_names(model_class) for non_used_layer_name in non_used_layer_names: if non_used_layer_name in layer_name: return False return True def test_training(self): for model_class in self.all_training_supported_model_classes: config, inputs_dict = self._prepare_inputs_for_training(model_class) model = model_class(config) model.to(torch_device) model.train() loss = model(**inputs_dict).loss loss.backward() # verify the gradients of used layers' weight are not None for name, param in model.named_parameters(): if self._is_layer_used(model_class, name): self.assertIsNotNone(param.grad, f"Gradients should not be None - got {param.grad} for {name}")