# Copyright 2023 The 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 InstructBLIP model.""" import inspect import tempfile import unittest import numpy as np import requests from transformers import ( CONFIG_MAPPING, BitsAndBytesConfig, InstructBlipConfig, InstructBlipProcessor, InstructBlipQFormerConfig, InstructBlipVisionConfig, ) from transformers.testing_utils import ( Expectations, cleanup, require_accelerate, require_bitsandbytes, require_torch, require_vision, slow, torch_device, ) from transformers.utils import is_torch_available, is_vision_available from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ...test_modeling_common import ( ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) if is_torch_available(): import torch from torch import nn from transformers import InstructBlipForConditionalGeneration, InstructBlipModel, InstructBlipVisionModel if is_vision_available(): from PIL import Image class InstructBlipVisionModelTester: def __init__( self, parent, batch_size=12, image_size=30, patch_size=2, num_channels=3, is_training=True, hidden_size=32, projection_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, initializer_range=1e-10, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.initializer_range = initializer_range self.scope = scope # in case of a vision transformer, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) config = self.get_config() return config, pixel_values def get_config(self): return InstructBlipVisionConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values): model = InstructBlipVisionModel(config=config) model.to(torch_device) model.eval() with torch.no_grad(): result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = (self.image_size, self.image_size) patch_size = (self.patch_size, self.patch_size) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class InstructBlipVisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as InstructBLIP's vision encoder does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (InstructBlipVisionModel,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = InstructBlipVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=InstructBlipConfig, has_text_modality=False, common_properties=["num_query_tokens", "image_token_index"], ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="InstructBLIP's vision encoder does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_get_set_embeddings(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) @unittest.skip(reason="InstructBlipVisionModel is an internal building block, doesn't support standalone training") def test_training(self): pass @unittest.skip(reason="InstructBlipVisionModel is an internal building block, doesn't support standalone training") def test_training_gradient_checkpointing(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass @slow def test_model_from_pretrained(self): model_name = "Salesforce/instructblip-flan-t5-xl" model = InstructBlipVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class InstructBlipQFormerModelTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_input_mask=True, use_labels=True, vocab_size=99, hidden_size=32, projection_dim=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, dropout=0.1, attention_dropout=0.1, max_position_embeddings=512, initializer_range=0.02, bos_token_id=0, scope=None, ): 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_labels = use_labels self.vocab_size = vocab_size self.hidden_size = hidden_size self.projection_dim = projection_dim self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.dropout = dropout self.attention_dropout = attention_dropout self.max_position_embeddings = max_position_embeddings self.initializer_range = initializer_range self.scope = scope self.bos_token_id = bos_token_id def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) qformer_input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.seq_length]) qformer_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2) if input_mask is not None: batch_size, seq_length = input_mask.shape rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,)) for batch_idx, start_index in enumerate(rnd_start_indices): input_mask[batch_idx, :start_index] = 1 input_mask[batch_idx, start_index:] = 0 config = self.get_config() return config, input_ids, input_mask, qformer_input_ids, qformer_attention_mask def get_config(self): return InstructBlipQFormerConfig( vocab_size=self.vocab_size, hidden_size=self.hidden_size, projection_dim=self.projection_dim, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, dropout=self.dropout, attention_dropout=self.attention_dropout, max_position_embeddings=self.max_position_embeddings, initializer_range=self.initializer_range, bos_token_id=self.bos_token_id, ) # this class is based on `OPTModelTester` found in tests/models/opt/test_modeling_opt.py class InstructBlipTextModelDecoderOnlyTester: def __init__( self, parent, batch_size=12, seq_length=7, is_training=True, use_labels=False, vocab_size=99, hidden_size=16, num_hidden_layers=2, num_attention_heads=4, intermediate_size=4, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=100, eos_token_id=2, pad_token_id=1, bos_token_id=0, embed_dim=16, num_labels=3, word_embed_proj_dim=16, type_sequence_label_size=2, ): self.parent = parent self.batch_size = batch_size self.seq_length = seq_length self.is_training = is_training 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.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.bos_token_id = bos_token_id self.embed_dim = embed_dim self.num_labels = num_labels self.type_sequence_label_size = type_sequence_label_size self.word_embed_proj_dim = word_embed_proj_dim self.is_encoder_decoder = False def prepare_config_and_inputs(self): config = self.get_config() input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(3) input_ids[:, -1] = self.eos_token_id # Eos Token attention_mask = input_ids.ne(self.pad_token_id) return config, input_ids, attention_mask def get_config(self): return CONFIG_MAPPING["opt"]( vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, ffn_dim=self.intermediate_size, dropout=self.hidden_dropout_prob, attention_dropout=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, eos_token_id=self.eos_token_id, bos_token_id=self.bos_token_id, pad_token_id=self.pad_token_id, embed_dim=self.embed_dim, is_encoder_decoder=False, word_embed_proj_dim=self.word_embed_proj_dim, ) # this model tester uses a decoder-only language model (OPT) class InstructBlipForConditionalGenerationDecoderOnlyModelTester: def __init__( self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10, image_token_index=4, ): if vision_kwargs is None: vision_kwargs = {} if qformer_kwargs is None: qformer_kwargs = {} if text_kwargs is None: text_kwargs = {} self.parent = parent self.vision_model_tester = InstructBlipVisionModelTester(parent, **vision_kwargs) self.qformer_model_tester = InstructBlipQFormerModelTester(parent, **qformer_kwargs) self.text_model_tester = InstructBlipTextModelDecoderOnlyTester(parent, **text_kwargs) self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test self.seq_length = self.text_model_tester.seq_length + num_query_tokens # need seq_length for common tests self.is_training = is_training self.num_query_tokens = num_query_tokens self.image_token_index = image_token_index def prepare_config_and_inputs(self): _, pixel_values = self.vision_model_tester.prepare_config_and_inputs() _, _, _, qformer_input_ids, qformer_attention_mask = self.qformer_model_tester.prepare_config_and_inputs() _, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() config = self.get_config() vision_tokens = ( torch.ones((input_ids.shape[0], self.num_query_tokens), device=torch_device, dtype=input_ids.dtype) * self.image_token_index ) input_ids[input_ids == self.image_token_index] = self.text_model_tester.pad_token_id input_ids = torch.cat([vision_tokens, input_ids], dim=-1) vision_attention_mask = torch.ones_like(vision_tokens) attention_mask = torch.cat([vision_attention_mask, attention_mask], dim=-1) return config, input_ids, attention_mask, qformer_input_ids, qformer_attention_mask, pixel_values def get_config(self): return InstructBlipConfig( vision_config=self.vision_model_tester.get_config(), qformer_config=self.qformer_model_tester.get_config(), text_config=self.text_model_tester.get_config(), num_query_tokens=self.num_query_tokens, image_token_index=self.image_token_index, ) def create_and_check_for_conditional_generation( self, config, input_ids, attention_mask, qformer_input_ids, qformer_attention_mask, pixel_values ): model = InstructBlipForConditionalGeneration(config).to(torch_device).eval() with torch.no_grad(): result = model( pixel_values, input_ids=input_ids, attention_mask=attention_mask, qformer_input_ids=qformer_input_ids, qformer_attention_mask=qformer_attention_mask, ) expected_seq_length = self.num_query_tokens + self.text_model_tester.seq_length self.parent.assertEqual( result.logits.shape, (self.vision_model_tester.batch_size, expected_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, qformer_input_ids, qformer_attention_mask, pixel_values = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, "qformer_input_ids": qformer_input_ids, "qformer_attention_mask": qformer_attention_mask, } return config, inputs_dict @require_torch class InstructBlipForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = ( ( InstructBlipModel, InstructBlipForConditionalGeneration, ) if is_torch_available() else () ) pipeline_model_mapping = {"image-text-to-text": InstructBlipForConditionalGeneration} additional_model_inputs = ["qformer_input_ids", "input_ids"] test_resize_embeddings = True test_attention_outputs = False _is_composite = True def setUp(self): self.model_tester = InstructBlipForConditionalGenerationDecoderOnlyModelTester(self) self.config_tester = ConfigTester( self, config_class=InstructBlipConfig, has_text_modality=False, common_properties=["num_query_tokens", "image_token_index"], ) def test_config(self): self.config_tester.run_common_tests() def test_for_conditional_generation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_conditional_generation(*config_and_inputs) @unittest.skip( reason=" InstructBlipQFormerModel does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention yet." ) def test_eager_matches_sdpa_generate(self): pass @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="InstructBlipForConditionalGeneration doesn't support inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Tied weights are tested in individual model tests") def test_tied_weights_keys(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass @unittest.skip(reason="InstructBlipModel does not have input/output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="InstructBLIP has no separate base model without a head.") def test_model_base_model_prefix(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_load_vision_qformer_text_config(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() # Save InstructBlipConfig and check if we can load InstructBlipVisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = InstructBlipVisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save InstructBlipConfig and check if we can load InstructBlipQFormerConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) qformer_config = InstructBlipQFormerConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict()) @slow def test_model_from_pretrained(self): model_name = "Salesforce/instructblip-flan-t5-xl" model = InstructBlipForConditionalGeneration.from_pretrained(model_name) self.assertIsNotNone(model) # overwrite because InstructBLIP internally calls LM.generate() with embeds thus it cannot operate in no cache format def _check_generate_outputs(self, output, config, use_cache=False, num_return_sequences=1, num_beams=1): use_cache = True # force this to be True in case False is passed super()._check_generate_outputs( output, config, use_cache=use_cache, num_return_sequences=num_return_sequences, num_beams=num_beams ) def test_sdpa_can_dispatch_composite_models(self): """ Tests if composite models dispatch correctly on SDPA/eager when requested so when loading the model. This tests only by looking at layer names, as usually SDPA layers are called "SDPAAttention". In contrast to the above test, this one checks if the "config._attn_implementation" is a dict after the model is loaded, because we manually replicate requested attn implementation on each sub-config when loading. See https://github.com/huggingface/transformers/pull/32238 for more info The test tries to cover most general cases of composite models, VLMs with vision and text configs. Any model that has a different set of sub-configs has to overwrite this test. """ if not self.has_attentions: self.skipTest(reason="Model architecture does not support attentions") if not self._is_composite: self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA") for model_class in self.all_model_classes: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() model = model_class(config) with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) model_sdpa = model_class.from_pretrained(tmpdirname) model_sdpa = model_sdpa.eval().to(torch_device) # `None` as it is the requested one which will be assigned to each sub-config # Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present) self.assertTrue(model.language_model.config._attn_implementation == "sdpa") self.assertTrue(model.vision_model.config._attn_implementation == "sdpa") self.assertTrue(model.qformer.config._attn_implementation == "eager") model_eager = model_class.from_pretrained(tmpdirname, attn_implementation="eager") model_eager = model_eager.eval().to(torch_device) self.assertTrue(model_eager.config._attn_implementation == "eager") self.assertTrue(model_eager.language_model.config._attn_implementation == "eager") self.assertTrue(model_eager.vision_model.config._attn_implementation == "eager") self.assertTrue(model_eager.qformer.config._attn_implementation == "eager") for name, submodule in model_eager.named_modules(): class_name = submodule.__class__.__name__ if ( class_name.endswith("Attention") and getattr(submodule, "config", None) and submodule.config._attn_implementation == "sdpa" ): raise ValueError("The eager model should not have SDPA attention layers") # We will verify our results on an image of cute cats def prepare_img(): url = "https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg" image = Image.open(requests.get(url, stream=True).raw) return image @require_vision @require_torch @slow class InstructBlipModelIntegrationTest(unittest.TestCase): def tearDown(self): cleanup(torch_device, gc_collect=False) @require_bitsandbytes @require_accelerate def test_inference_vicuna_7b(self): processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b") model = InstructBlipForConditionalGeneration.from_pretrained( "Salesforce/instructblip-vicuna-7b", quantization_config=BitsAndBytesConfig(load_in_8bit=True) ) url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") prompt = "What is unusual about this image?" inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, torch.float16) # verify generation outputs = model.generate(**inputs, max_new_tokens=30) generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip() expected_outputs = Expectations( { ("xpu", 3): [32001] * 32 + [2, 1724, 338, 22910, 1048, 445, 1967, 29973, 450, 22910, 9565, 310, 445, 1967, 338, 393, 263, 767, 338, 13977, 292, 22095, 373, 278, 1250, 310, 263, 13328, 20134, 29963, 1550, 19500, 1623, 263, 19587, 4272, 11952, 29889], ("cuda", None): [32001] * 32 + [2, 1724, 338, 22910, 1048, 445, 1967, 29973, 450, 22910, 9565, 310, 445, 1967, 338, 393, 263, 767, 338, 13977, 292, 22095, 373, 278, 1250, 310, 263, 13328, 20134, 29963, 1550, 19500, 373, 263, 19587, 4272, 11952, 29889], } ) # fmt: off expected_output = expected_outputs.get_expectation() expected_texts = Expectations( { ("xpu", 3): "What is unusual about this image? The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV while driving down a busy city street.", ("cuda", None): "What is unusual about this image? The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV while driving on a busy city street.", } ) # fmt: off expected_text = expected_texts.get_expectation() self.assertEqual(outputs[0].tolist(), expected_output) self.assertEqual(generated_text, expected_text) def test_inference_flant5_xl(self): processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl") model = InstructBlipForConditionalGeneration.from_pretrained( "Salesforce/instructblip-flan-t5-xl", dtype=torch.bfloat16, ).to(torch_device) url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg" image = Image.open(requests.get(url, stream=True).raw).convert("RGB") prompt = "What is unusual about this image?" inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device) for k, v in inputs.items(): if torch.is_floating_point(v): inputs[k] = v.to(torch.bfloat16) outputs = model.generate( **inputs, do_sample=False, num_beams=5, max_length=256, min_length=1, repetition_penalty=1.5, length_penalty=1.0, temperature=1, ) generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0] expected_outputs = [0, 37, 1023, 9850, 7, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4459, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 388, 19, 5119, 3, 9, 4459, 8677, 28, 3, 9, 4459, 6177, 6, 11, 3, 88, 19, 338, 46, 3575, 53, 1476, 5223, 12, 8, 223, 13, 8, 4049, 5, 37, 1023, 19, 7225, 16, 24, 34, 1267, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 388, 19, 338, 46, 3575, 53, 1476, 5223, 12, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 388, 19, 338, 46, 3575, 53, 1476, 5223, 12, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 1023, 19, 7225, 16, 24, 34, 1267, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 388, 19, 338, 46, 3575, 53, 1476, 5223, 12, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 1] # fmt: skip self.assertEqual(outputs[0].tolist(), expected_outputs) self.assertEqual( generated_text, "The image depicts a man ironing clothes on the back of a yellow van in the middle of a busy city street. The man is wearing a yellow shirt with a yellow tie, and he is using an ironing board attached to the back of the van. The image is unusual in that it shows a man ironing clothes on the back of a van in the middle of a busy city street. The man is using an ironing board attached to the back of a van in the middle of a busy city street. The man is using an ironing board attached to the back of a van in the middle of a busy city street. The image is unusual in that it shows a man ironing clothes on the back of a van in the middle of a busy city street. The man is using an ironing board attached to the back of a van in the middle of a busy city street.", ) def test_inference_interpolate_pos_encoding(self): processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl") model = InstructBlipForConditionalGeneration.from_pretrained( "Salesforce/instructblip-flan-t5-xl", dtype=torch.bfloat16, ).to(torch_device) processor.image_processor.size = {"height": 500, "width": 500} image = prepare_img() prompt = "What's in the image?" inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device) predictions = model.generate(**inputs, interpolate_pos_encoding=True) generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip() self.assertEqual( predictions[0].tolist(), [0, 37, 1023, 753, 3, 9, 2335, 3823, 30, 8, 2608, 28, 3, 9, 1782, 5, 1] ) self.assertEqual(generated_text, "The image features a woman sitting on the beach with a dog.")