# 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 unittest from functools import cached_property from transformers import ( BridgeTowerConfig, BridgeTowerTextConfig, BridgeTowerVisionConfig, is_torch_available, is_vision_available, ) from transformers.testing_utils import require_torch, require_vision, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, 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, ) if is_vision_available(): from PIL import Image from transformers import BridgeTowerProcessor class BridgeTowerTextModelTester: def __init__( self, parent, hidden_act="gelu", hidden_size=64, initializer_factor=1, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=2, intermediate_size=128, tie_word_embeddings=False, output_hidden_states=False, ): self.parent = parent self.hidden_act = hidden_act self.hidden_size = hidden_size self.initializer_factor = initializer_factor self.layer_norm_eps = layer_norm_eps self.num_attention_heads = num_attention_heads self.num_hidden_layers = num_hidden_layers self.intermediate_size = intermediate_size self.tie_word_embeddings = tie_word_embeddings self.vocab_size = 99 self.seq_length = 4 self.batch_size = 1 self.is_training = False self.output_hidden_states = output_hidden_states 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]) config = self.get_config() return config, input_ids, attention_mask def get_config(self): return BridgeTowerTextConfig( hidden_act=self.hidden_act, hidden_size=self.hidden_size, initializer_factor=self.initializer_factor, layer_norm_eps=self.layer_norm_eps, num_attention_heads=self.num_attention_heads, num_hidden_layers=self.num_hidden_layers, intermediate_size=self.intermediate_size, tie_word_embeddings=self.tie_word_embeddings, output_hidden_states=self.output_hidden_states, vocab_size=self.vocab_size, ) class BridgeTowerImageModelTester: def __init__( self, parent, hidden_size=64, initializer_factor=1, layer_norm_eps=1e-05, num_hidden_layers=2, init_layernorm_from_vision_encoder=False, output_hidden_states=False, image_size=64, ): self.parent = parent self.hidden_size = hidden_size self.initializer_factor = initializer_factor self.layer_norm_eps = layer_norm_eps self.num_hidden_layers = num_hidden_layers self.init_layernorm_from_vision_encoder = init_layernorm_from_vision_encoder self.num_channels = 3 self.num_image_features = 17 self.batch_size = 1 self.image_size = image_size self.is_training = False self.output_hidden_states = output_hidden_states def prepare_config_and_inputs(self): 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, pixel_values, pixel_mask def get_config(self): return BridgeTowerVisionConfig( hidden_size=self.hidden_size, initializer_factor=self.initializer_factor, layer_norm_eps=self.layer_norm_eps, num_hidden_layers=self.num_hidden_layers, init_layernorm_from_vision_encoder=self.init_layernorm_from_vision_encoder, num_channels=self.num_channels, num_image_features=self.num_image_features, batch_size=self.batch_size, image_size=self.image_size, is_training=self.is_training, output_hidden_states=self.output_hidden_states, ) class BridgeTowerModelTester: def __init__( self, parent, text_kwargs=None, vision_kwargs=None, share_cross_modal_transformer_layers=True, share_link_tower_layers=False, link_tower_type="add", init_layernorm_from_vision_encoder=False, contrastive_hidden_size=512, logit_scale_init_value=2.6592, hidden_size=64, num_hidden_layers=2, num_attention_heads=4, intermediate_size=128, ): if text_kwargs is None: text_kwargs = {} if vision_kwargs is None: vision_kwargs = {} self.parent = parent self.text_model_tester = BridgeTowerTextModelTester(parent, **text_kwargs) self.vision_model_tester = BridgeTowerImageModelTester(parent, **vision_kwargs) self.share_cross_modal_transformer_layers = share_cross_modal_transformer_layers self.share_link_tower_layers = share_link_tower_layers self.link_tower_type = link_tower_type self.init_layernorm_from_vision_encoder = init_layernorm_from_vision_encoder self.contrastive_hidden_size = contrastive_hidden_size self.logit_scale_init_value = logit_scale_init_value self.batch_size = 1 self.expected_num_hidden_layers = 8 self.is_training = False self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size def prepare_config_and_inputs(self): text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() vision_config, pixel_values, pixel_mask = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return (config, input_ids, attention_mask, pixel_values, pixel_mask) def get_config(self): return BridgeTowerConfig( text_config=self.text_model_tester.get_config().to_dict(), vision_config=self.vision_model_tester.get_config().to_dict(), share_cross_modal_transformer_layers=self.share_cross_modal_transformer_layers, share_link_tower_layers=self.share_link_tower_layers, link_tower_type=self.link_tower_type, init_layernorm_from_vision_encoder=self.init_layernorm_from_vision_encoder, contrastive_hidden_size=self.contrastive_hidden_size, logit_scale_init_value=self.logit_scale_init_value, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, ) 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.text_model_tester.seq_length, self.text_model_tester.hidden_size), ) self.parent.assertEqual( result["image_features"].shape, (self.batch_size, self.vision_model_tester.num_image_features, self.vision_model_tester.hidden_size), ) self.parent.assertEqual( result["pooler_output"].shape, (self.batch_size, self.text_model_tester.hidden_size + self.vision_model_tester.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.text_model_tester.seq_length, self.text_model_tester.vocab_size), ) 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 @require_torch 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_resize_embeddings = False has_attentions = False @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_cpu_offload(self): pass @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_disk_offload(self): pass @unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.") def test_model_parallelism(self): pass # 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): model_name = "BridgeTower/bridgetower-base" model = BridgeTowerModel.from_pretrained(model_name) self.assertIsNotNone(model) # 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 = self.model_tester.expected_num_hidden_layers self.assertEqual( sum((len(hidden_states_text), len(hidden_states_vision), len(hidden_states_cross))), expected_num_layers, ) seq_length = self.model_tester.text_model_tester.seq_length num_image_features = self.model_tester.vision_model_tester.num_image_features self.assertListEqual( list(hidden_states_text[0].shape[-2:]), [seq_length, self.model_tester.text_model_tester.hidden_size], ) self.assertListEqual( list(hidden_states_vision[0].shape), [num_image_features, 1, self.model_tester.vision_model_tester.hidden_size], ) self.assertListEqual( list(hidden_states_cross[0][0].shape[-2:]), [seq_length, self.model_tester.text_model_tester.hidden_size], ) self.assertListEqual( list(hidden_states_cross[0][1].shape[-2:]), [num_image_features, self.model_tester.vision_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) @unittest.skip(reason="""Bridge Tower does not have input/output embeddings. So this test is not applicable.""") def test_model_get_set_embeddings(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 @unittest.skip(reason="Bridge Tower does not use inputs_embeds") def test_inputs_embeds_matches_input_ids(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 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 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 + [ # This number `1` actually depends on the number of layers in `cross_modal_image_layers` (by minus 1) "cross_modal_image_layers.1", "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}") @slow def test_inference_interpolate_pos_encoding(self): # ViT models have an `interpolate_pos_encoding` argument in their forward method, # allowing to interpolate the pre-trained position embeddings in order to use # the model on higher resolutions. The DINO model by Facebook AI leverages this # to visualize self-attention on higher resolution images. model_name = "BridgeTower/bridgetower-base" model = BridgeTowerModel.from_pretrained(model_name).to(torch_device) image_processor = BridgeTowerProcessor.from_pretrained(model_name, size={"shortest_edge": 180}) image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") inputs = image_processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device) # interpolate_pos_encodiung false should return value error with self.assertRaises(ValueError, msg="doesn't match model"): with torch.no_grad(): model(**inputs, interpolate_pos_encoding=False) # forward pass with torch.no_grad(): outputs = model(**inputs, interpolate_pos_encoding=True) # verify the logits expected_shape = torch.Size((1, 122, 768)) self.assertEqual(outputs.image_features.shape, expected_shape) expected_slice = torch.tensor( [[-0.6518, 0.4978, -0.4544], [-2.6672, -0.0843, -0.4210], [-2.4510, -0.1002, -0.3458]] ).to(torch_device) torch.testing.assert_close(outputs.image_features[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)