# 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 BLIP-2 model.""" import inspect import tempfile import unittest import numpy as np import requests from parameterized import parameterized from transformers import CONFIG_MAPPING, Blip2Config, Blip2QFormerConfig, Blip2VisionConfig from transformers.testing_utils import ( Expectations, cleanup, require_torch, require_torch_accelerator, require_torch_fp16, require_torch_multi_accelerator, 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 ( TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION, ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask, ) from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( Blip2ForConditionalGeneration, Blip2ForImageTextRetrieval, Blip2Model, Blip2TextModelWithProjection, Blip2VisionModel, Blip2VisionModelWithProjection, ) if is_vision_available(): from PIL import Image from transformers import Blip2Processor class Blip2VisionModelTester: 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 ViT, 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 Blip2VisionConfig( 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 = Blip2VisionModel(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 Blip2VisionModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as BLIP-2's vision encoder does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (Blip2VisionModel,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = Blip2VisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Blip2VisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="BLIP-2'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): for model_class in self.all_model_classes: config, _ = self.model_tester.prepare_config_and_inputs_for_common() 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 def test_training(self): pass @unittest.skip 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/blip2-opt-2.7b" model = Blip2VisionModel.from_pretrained(model_name) self.assertIsNotNone(model) class Blip2QFormerModelTester: 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, use_qformer_text_input=False, ): 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 self.use_qformer_text_input = use_qformer_text_input def prepare_config_and_inputs(self): 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]) 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 def get_config(self): return Blip2QFormerConfig( 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, use_qformer_text_input=self.use_qformer_text_input, ) # this class is based on `OPTModelTester` found in tests/models/opt/test_modeling_opt.py class Blip2TextModelDecoderOnlyTester: 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=512, 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 Blip2ForConditionalGenerationDecoderOnlyModelTester: 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 = Blip2VisionModelTester(parent, **vision_kwargs) self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs) self.text_model_tester = Blip2TextModelDecoderOnlyTester(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() _, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs() 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) config = self.get_config() return config, input_ids, attention_mask, pixel_values def get_config(self): return Blip2Config( 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, pixel_values): model = Blip2ForConditionalGeneration(config).to(torch_device).eval() with torch.no_grad(): result = model(pixel_values, input_ids, 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, pixel_values = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict @require_torch class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (Blip2ForConditionalGeneration,) if is_torch_available() else () additional_model_inputs = ["input_ids"] test_resize_embeddings = False test_attention_outputs = False _is_composite = True def setUp(self): self.model_tester = Blip2ForConditionalGenerationDecoderOnlyModelTester(self) common_properties = ["image_token_index", "num_query_tokens", "image_text_hidden_size"] self.config_tester = ConfigTester( self, config_class=Blip2Config, has_text_modality=False, common_properties=common_properties ) 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="Blip2QFormerModel 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="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(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="Blip2Model does not have input/output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="BLIP2 has no separate base model without a head.") def test_model_base_model_prefix(self): pass 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") 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, _ = self.model_tester.prepare_config_and_inputs_for_common() # Save Blip2Config and check if we can load Blip2VisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = Blip2VisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save Blip2Config and check if we can load Blip2QFormerConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) qformer_config = Blip2QFormerConfig.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/blip2-opt-2.7b" model = Blip2ForConditionalGeneration.from_pretrained(model_name) self.assertIsNotNone(model) # overwrite because BLIP 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 ) # this class is based on `T5ModelTester` found in tests/models/t5/test_modeling_t5.py class Blip2TextModelTester: def __init__( self, parent, vocab_size=99, batch_size=12, encoder_seq_length=7, decoder_seq_length=9, # For common tests is_training=True, use_attention_mask=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, d_ff=37, relative_attention_num_buckets=8, dropout_rate=0.1, initializer_factor=0.002, eos_token_id=1, pad_token_id=0, decoder_start_token_id=0, scope=None, decoder_layers=None, ): self.parent = parent self.batch_size = batch_size self.encoder_seq_length = encoder_seq_length self.decoder_seq_length = decoder_seq_length # For common tests self.seq_length = self.decoder_seq_length self.is_training = is_training self.use_attention_mask = use_attention_mask 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.d_ff = d_ff self.relative_attention_num_buckets = relative_attention_num_buckets self.dropout_rate = dropout_rate self.initializer_factor = initializer_factor self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.decoder_start_token_id = decoder_start_token_id self.scope = None self.decoder_layers = decoder_layers def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.encoder_seq_length], self.vocab_size) decoder_input_ids = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) attention_mask = None decoder_attention_mask = None if self.use_attention_mask: attention_mask = ids_tensor([self.batch_size, self.encoder_seq_length], vocab_size=2) decoder_attention_mask = ids_tensor([self.batch_size, self.decoder_seq_length], vocab_size=2) lm_labels = None if self.use_labels: lm_labels = ids_tensor([self.batch_size, self.decoder_seq_length], self.vocab_size) config = self.get_config() return ( config, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) def get_config(self): return CONFIG_MAPPING["t5"]( vocab_size=self.vocab_size, d_model=self.hidden_size, d_ff=self.d_ff, d_kv=self.hidden_size // self.num_attention_heads, num_layers=self.num_hidden_layers, num_decoder_layers=self.decoder_layers, num_heads=self.num_attention_heads, relative_attention_num_buckets=self.relative_attention_num_buckets, dropout_rate=self.dropout_rate, initializer_factor=self.initializer_factor, eos_token_id=self.eos_token_id, bos_token_id=self.pad_token_id, pad_token_id=self.pad_token_id, decoder_start_token_id=self.decoder_start_token_id, is_encoder_decoder=True, ) # this model tester uses an encoder-decoder language model (T5) class Blip2ModelTester: 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 = Blip2VisionModelTester(parent, **vision_kwargs) self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs) self.text_model_tester = Blip2TextModelTester(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 # need seq_length for common tests self.encoder_seq_length = self.text_model_tester.encoder_seq_length 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() ( _, input_ids, decoder_input_ids, attention_mask, decoder_attention_mask, lm_labels, ) = self.text_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values, decoder_input_ids, decoder_attention_mask, lm_labels def get_config(self): return Blip2Config( 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, pixel_values, decoder_input_ids, decoder_attention_mask, labels ): model = Blip2ForConditionalGeneration(config).to(torch_device).eval() with torch.no_grad(): result = model(pixel_values, input_ids, attention_mask, decoder_input_ids, decoder_attention_mask) self.parent.assertEqual( result.logits.shape, ( self.vision_model_tester.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, decoder_input_ids, decoder_attention_mask, labels, ) = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "input_ids": input_ids, "attention_mask": attention_mask, "decoder_input_ids": decoder_input_ids, "decoder_attention_mask": decoder_attention_mask, } return config, inputs_dict @require_torch class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixin, unittest.TestCase): all_model_classes = (Blip2ForConditionalGeneration, Blip2Model) if is_torch_available() else () additional_model_inputs = ["input_ids", "decoder_input_ids"] pipeline_model_mapping = ( { "feature-extraction": Blip2Model, "image-to-text": Blip2ForConditionalGeneration, "visual-question-answering": Blip2ForConditionalGeneration, "image-text-to-text": Blip2ForConditionalGeneration, "any-to-any": Blip2ForConditionalGeneration, } if is_torch_available() else {} ) test_resize_embeddings = True test_attention_outputs = False _is_composite = True # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): if pipeline_test_case_name == "VisualQuestionAnsweringPipelineTests": # Get `RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'`. return True return False def setUp(self): self.model_tester = Blip2ModelTester(self) common_properties = ["image_token_index", "num_query_tokens", "image_text_hidden_size"] self.config_tester = ConfigTester( self, config_class=Blip2Config, has_text_modality=False, common_properties=common_properties ) 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="Blip2QFormerModel 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="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(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="Blip2Model does not have input/output embeddings") def test_model_get_set_embeddings(self): pass @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="BLIP2 has no separate base model without a head.") def test_model_base_model_prefix(self): pass 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 == "eager") 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") def test_forward_signature(self): for model_class in self.all_model_classes: config, _ = self.model_tester.prepare_config_and_inputs_for_common() 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, _ = self.model_tester.prepare_config_and_inputs_for_common() # Save Blip2Config and check if we can load Blip2VisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = Blip2VisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save Blip2Config and check if we can load Blip2QFormerConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) qformer_config = Blip2QFormerConfig.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/blip2-opt-2.7b" model = Blip2ForConditionalGeneration.from_pretrained(model_name) self.assertIsNotNone(model) def test_get_text_features(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() inputs_dict = { "input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device), "attention_mask": torch.LongTensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]).to(torch_device), "decoder_input_ids": torch.LongTensor([[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]]).to(torch_device), } model = Blip2Model(config).to(torch_device) model.eval() text_features = model.get_text_features(**inputs_dict) self.assertEqual(text_features[0].shape, (10, config.text_config.vocab_size)) def test_get_image_features(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask"] for key in keys_to_pop: inputs_dict.pop(key) model = Blip2Model(config).to(torch_device) model.eval() image_features = model.get_image_features(**inputs_dict) self.assertEqual( image_features[0].shape, (config.vision_config.hidden_size,), ) def test_get_qformer_features(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() keys_to_pop = ["input_ids", "attention_mask", "decoder_input_ids", "decoder_attention_mask"] for key in keys_to_pop: inputs_dict.pop(key) model = Blip2Model(config).to(torch_device) model.eval() qformer_features = model.get_qformer_features(**inputs_dict) self.assertEqual( qformer_features[0].shape, (10, config.vision_config.hidden_size), ) @unittest.skip("T5 backbone deepcopies the configs, and fixing it would be more involved") def test_internal_model_config_and_subconfig_are_same(self): pass @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION) @unittest.skip("Won't fix: Blip2 + T5 backbone needs custom input preparation for this test") def test_eager_matches_sdpa_inference(self, *args): pass class Blip2TextModelWithProjectionTester: def __init__(self, parent, vision_kwargs=None, qformer_kwargs=None, is_training=True): if vision_kwargs is None: vision_kwargs = {} if qformer_kwargs is None: qformer_kwargs = {"use_qformer_text_input": True} self.parent = parent self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs) self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs) self.is_training = is_training self.batch_size = self.vision_model_tester.batch_size # need bs for batching_equivalence test def get_config(self): return Blip2Config( vision_config=self.vision_model_tester.get_config(), qformer_config=self.qformer_model_tester.get_config(), ) def prepare_config_and_inputs(self): _, input_ids, attention_mask = self.qformer_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, } return config, inputs_dict def create_and_check_model(self, config, input_ids, attention_mask): model = Blip2TextModelWithProjection(config=config) model.set_attn_implementation("eager") model.to(torch_device) model.eval() with torch.no_grad(): result = model(input_ids, attention_mask=attention_mask, output_attentions=True, output_hidden_states=True) self.parent.assertEqual( result.last_hidden_state.shape, (self.vision_model_tester.batch_size, input_ids.shape[1], self.qformer_model_tester.hidden_size), ) self.parent.assertEqual( result.text_embeds.shape, ( self.vision_model_tester.batch_size, input_ids.shape[1], config.image_text_hidden_size, ), ) with torch.no_grad(): result2 = model( input_ids, attention_mask=attention_mask, return_dict=not config.use_return_dict, output_attentions=True, output_hidden_states=True, ) self.parent.assertTrue(torch.allclose(result.text_embeds, result2[0])) self.parent.assertTrue(torch.allclose(result.last_hidden_state, result2[1])) self.parent.assertTrue(torch.allclose(result.hidden_states[0], result2[2][0])) self.parent.assertTrue(torch.allclose(result.hidden_states[1], result2[2][1])) self.parent.assertTrue(torch.allclose(result.attentions[0], result2[3][0])) self.parent.assertTrue(torch.allclose(result.attentions[1], result2[3][1])) @require_torch class Blip2TextModelWithProjectionTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (Blip2TextModelWithProjection,) if is_torch_available() else () test_resize_embeddings = True test_attention_outputs = False def setUp(self): self.model_tester = Blip2TextModelWithProjectionTester(self) 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="Training is not yet supported") def test_training(self): pass @unittest.skip(reason="Training is not yet supported") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Blip2TextModelWithProjection does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Blip2TextModelWithProjection does not support input and output embeddings") def test_model_get_set_embeddings(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="Blip2TextModelWithProjection does not have input/output embeddings") def test_model_common_attributes(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 = ["input_ids", "attention_mask", "position_ids"] self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) @slow @require_torch_accelerator def test_model_from_pretrained(self): model_name = "Salesforce/blip2-itm-vit-g" model = Blip2TextModelWithProjection.from_pretrained(model_name) self.assertIsNotNone(model) self.assertTrue(hasattr(model, "text_projection")) _, input_ids, attention_mask = self.model_tester.prepare_config_and_inputs() model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(input_ids=input_ids, attention_mask=attention_mask) self.assertEqual( outputs.text_embeds.shape, ( self.model_tester.qformer_model_tester.batch_size, input_ids.shape[1], model.config.image_text_hidden_size, ), ) class Blip2VisionModelWithProjectionTester: def __init__(self, parent, vision_kwargs=None, qformer_kwargs=None, is_training=True): if vision_kwargs is None: vision_kwargs = {} if qformer_kwargs is None: qformer_kwargs = {"use_qformer_text_input": True} self.parent = parent self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs) self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs) self.is_training = is_training self.num_hidden_layers = self.vision_model_tester.num_hidden_layers self.num_attention_heads = self.vision_model_tester.num_attention_heads self.seq_length = self.vision_model_tester.seq_length self.hidden_size = self.vision_model_tester.hidden_size self.batch_size = self.vision_model_tester.batch_size # need bs for batching_equivalence test def get_config(self): return Blip2Config( vision_config=self.vision_model_tester.get_config(), qformer_config=self.qformer_model_tester.get_config(), ) def prepare_config_and_inputs(self): _, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, pixel_values 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 def create_and_check_model(self, config, pixel_values): model = Blip2VisionModelWithProjection(config=config) model.to(torch_device) model.set_attn_implementation("eager") model.eval() with torch.no_grad(): result = model(pixel_values, output_attentions=True, output_hidden_states=True) self.parent.assertEqual( result.last_hidden_state.shape, ( self.vision_model_tester.batch_size, self.vision_model_tester.seq_length, self.qformer_model_tester.hidden_size, ), ) self.parent.assertEqual( result.image_embeds.shape, ( self.vision_model_tester.batch_size, config.vision_config.hidden_size, config.image_text_hidden_size, ), ) with torch.no_grad(): result2 = model( pixel_values, return_dict=not config.use_return_dict, output_attentions=True, output_hidden_states=True, ) self.parent.assertTrue(torch.allclose(result.image_embeds, result2[0])) self.parent.assertTrue(torch.allclose(result.last_hidden_state, result2[1])) self.parent.assertTrue(torch.allclose(result.hidden_states[0], result2[2][0])) self.parent.assertTrue(torch.allclose(result.hidden_states[1], result2[2][1])) self.parent.assertTrue(torch.allclose(result.attentions[0], result2[3][0])) self.parent.assertTrue(torch.allclose(result.attentions[1], result2[3][1])) @require_torch class Blip2VisionModelWithProjectionTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (Blip2VisionModelWithProjection,) if is_torch_available() else () test_resize_embeddings = False def setUp(self): self.model_tester = Blip2VisionModelWithProjectionTester(self) 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="Training is not yet supported") def test_training(self): pass @unittest.skip(reason="Training is not yet supported") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="Training is not yet supported") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="Training is not yet supported") def test_training_gradient_checkpointing_use_reentrant_false(self): pass @unittest.skip(reason="Blip2VisionModelWithProjection does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Blip2VisionModelWithProjection does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="Retain_grad is tested in individual model tests") def test_retain_grad_hidden_states_attentions(self): pass def test_model_common_attributes(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[: len(expected_arg_names)], expected_arg_names) @slow @require_torch_accelerator def test_model_from_pretrained(self): model_name = "Salesforce/blip2-itm-vit-g" model = Blip2VisionModelWithProjection.from_pretrained(model_name) self.assertIsNotNone(model) self.assertTrue(hasattr(model, "vision_projection")) _, pixel_values = self.model_tester.prepare_config_and_inputs() model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(pixel_values=pixel_values) self.assertEqual( outputs.image_embeds.shape, ( self.model_tester.vision_model_tester.batch_size, model.config.num_query_tokens, model.config.image_text_hidden_size, ), ) class Blip2TextRetrievalModelTester: def __init__(self, parent, vision_kwargs=None, qformer_kwargs=None, is_training=True): if vision_kwargs is None: vision_kwargs = {} if qformer_kwargs is None: qformer_kwargs = {"use_qformer_text_input": True} self.parent = parent self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs) self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs) self.is_training = is_training self.batch_size = self.vision_model_tester.batch_size # need bs for batching_equivalence test def get_config(self): return Blip2Config( vision_config=self.vision_model_tester.get_config(), qformer_config=self.qformer_model_tester.get_config(), ) def prepare_config_and_inputs(self): _, input_ids, attention_mask = self.qformer_model_tester.prepare_config_and_inputs() _, pixel_values = self.vision_model_tester.prepare_config_and_inputs() config = self.get_config() return config, input_ids, attention_mask, pixel_values def create_and_check_model(self, config, input_ids, attention_mask, pixel_values): model = Blip2ForImageTextRetrieval(config).to(torch_device).eval() with torch.no_grad(): result = model(pixel_values, input_ids, attention_mask, use_image_text_matching_head=True) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, 2), ) with torch.no_grad(): result = model(pixel_values, input_ids, attention_mask) self.parent.assertEqual( result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.qformer_model_tester.batch_size), ) self.parent.assertEqual( result.logits_per_text.shape, (self.qformer_model_tester.batch_size, self.vision_model_tester.batch_size) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, input_ids, attention_mask, pixel_values = config_and_inputs inputs_dict = { "input_ids": input_ids, "attention_mask": attention_mask, "pixel_values": pixel_values, } return config, inputs_dict @require_torch class Blip2TextRetrievalModelTest(ModelTesterMixin, unittest.TestCase): all_model_classes = (Blip2ForImageTextRetrieval,) if is_torch_available() else () additional_model_inputs = ["input_ids"] test_resize_embeddings = True test_attention_outputs = False def setUp(self): self.model_tester = Blip2TextRetrievalModelTester(self) 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="Hidden_states is tested in individual model tests") def test_hidden_states_output(self): pass @unittest.skip(reason="Inputs_embeds is tested in individual model tests") def test_inputs_embeds(self): pass @unittest.skip(reason="Blip2ForImageTextRetrieval does not support input and output embeddings") def test_model_get_set_embeddings(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="Blip2Model does not have input/output embeddings") def test_model_common_attributes(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", "input_ids", "attention_mask"] expected_arg_names.extend( ["use_image_text_matching_head"] if "use_image_text_matching_head" in arg_names else [] ) self.assertListEqual(arg_names[: len(expected_arg_names)], expected_arg_names) def test_load_vision_qformer_text_config(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() # Save Blip2Config and check if we can load Blip2VisionConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) vision_config = Blip2VisionConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict()) # Save Blip2Config and check if we can load Blip2QFormerConfig from it with tempfile.TemporaryDirectory() as tmp_dir_name: config.save_pretrained(tmp_dir_name) qformer_config = Blip2QFormerConfig.from_pretrained(tmp_dir_name) self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict()) @slow @require_torch_accelerator def test_model_from_pretrained(self): model_name = "Salesforce/blip2-itm-vit-g" model = Blip2ForImageTextRetrieval.from_pretrained(model_name) self.assertIsNotNone(model) _, input_ids, attention_mask, pixel_values = self.model_tester.prepare_config_and_inputs() model.to(torch_device) model.eval() with torch.no_grad(): outputs = model( pixel_values=pixel_values, input_ids=input_ids, attention_mask=attention_mask, use_image_text_matching_head=True, ) self.assertEqual(outputs.logits_per_image.shape, (self.model_tester.qformer_model_tester.batch_size, 2)) with torch.no_grad(): outputs = model( pixel_values=pixel_values, input_ids=input_ids, attention_mask=attention_mask, ) self.assertEqual( outputs.logits_per_image.shape, (self.model_tester.vision_model_tester.batch_size, self.model_tester.qformer_model_tester.batch_size), ) @unittest.skip(reason="Training is not yet supported") def test_training(self): pass @unittest.skip(reason="Training is not yet supported") def test_training_gradient_checkpointing(self): pass @unittest.skip(reason="Training is not yet supported") def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip(reason="Training is not yet supported") def test_training_gradient_checkpointing_use_reentrant_false(self): pass # 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 Blip2ModelIntegrationTest(unittest.TestCase): def setUp(self): cleanup(torch_device, gc_collect=True) def tearDown(self): cleanup(torch_device, gc_collect=True) def test_inference_opt(self): processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", dtype=torch.float16).to( torch_device ) # prepare image image = prepare_img() inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16) predictions = model.generate(**inputs) generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip() # Test output expected_ids = [50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118] # fmt: skip self.assertEqual(predictions[0].tolist(), expected_ids) self.assertEqual("a woman sitting on the beach with a dog", generated_text) # image and context prompt = "Question: which city is this? Answer:" inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16) # max_length for BLIP includes prompt length from now on, use max_new_tokens predictions = model.generate(**inputs, max_new_tokens=11) generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip() # Test output expected_ids = [50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 2, 45641, 35, 61, 343, 16, 42, 116, 31652, 35, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118] # fmt: skip self.assertEqual(predictions[0].tolist(), expected_ids) self.assertEqual(generated_text, "Question: which city is this? Answer: it's not a city, it's a beach") def test_inference_interpolate_pos_encoding(self): processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", dtype=torch.float16).to( torch_device ) processor.image_processor.size = {"height": 500, "width": 500} image = prepare_img() inputs = processor(images=image, 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() expected_ids = [50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 2, 102, 693, 8, 2335, 15, 5, 4105, 50118] # fmt: skip self.assertEqual(predictions[0].tolist(), expected_ids) self.assertEqual(generated_text, "a woman and dog on the beach") def test_inference_opt_batched_beam_search(self): processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", dtype=torch.float16).to( torch_device ) # prepare image image = prepare_img() inputs = processor(images=[image, image], return_tensors="pt").to(torch_device, dtype=torch.float16) predictions = model.generate(**inputs, num_beams=2) # Test output (in this case, slightly different from greedy search) expected_ids = [50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 50265, 2, 102, 693, 2828, 15, 5, 4105, 19, 69, 2335, 50118] # fmt: skip self.assertEqual(predictions[0].tolist(), expected_ids) self.assertEqual(predictions[1].tolist(), expected_ids) def test_inference_t5(self): processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl", dtype=torch.float16).to( torch_device ) # prepare image image = prepare_img() inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16) predictions = model.generate(**inputs) generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip() expectations = Expectations( { ("xpu", 3): [ [0, 3, 9, 2335, 19, 1556, 28, 160, 1782, 30, 8, 2608, 1], "a woman is playing with her dog on the beach", ], ("cuda", 7): [ [0, 3, 9, 2335, 19, 1556, 28, 160, 1782, 30, 8, 2608, 1], "a woman is playing with her dog on the beach", ], } ) expected_outputs = expectations.get_expectation() # Test output self.assertEqual(predictions[0].tolist(), expected_outputs[0]) self.assertEqual(expected_outputs[1], generated_text) # image and context prompt = "Question: which city is this? Answer:" inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16) predictions = model.generate(**inputs) generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip() expectations = Expectations( { ("xpu", 3): [ [0, 3, 7, 152, 2515, 11389, 3523, 1], "san francisco", ], ("cuda", 7): [ [0, 3, 7, 152, 2515, 11389, 3523, 1], "san francisco", ], } ) expected_outputs = expectations.get_expectation() # Test output self.assertEqual(predictions[0].tolist(), expected_outputs[0]) self.assertEqual(generated_text, expected_outputs[1]) def test_inference_t5_batched_beam_search(self): processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl") model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-flan-t5-xl", dtype=torch.float16).to( torch_device ) # prepare image image = prepare_img() inputs = processor(images=[image, image], return_tensors="pt").to(torch_device, dtype=torch.float16) predictions = model.generate(**inputs, num_beams=2) expectations = Expectations( { ("xpu", 3): [ [0, 3, 9, 2335, 19, 1556, 28, 160, 1782, 30, 8, 2608, 1], [0, 3, 9, 2335, 19, 1556, 28, 160, 1782, 30, 8, 2608, 1], ], ("cuda", 7): [ [0, 3, 9, 2335, 19, 1556, 28, 160, 1782, 30, 8, 2608, 1], [0, 3, 9, 2335, 19, 1556, 28, 160, 1782, 30, 8, 2608, 1], ], } ) expected_predictions = expectations.get_expectation() # Test output (in this case, slightly different from greedy search) self.assertEqual(predictions[0].tolist(), expected_predictions[0]) self.assertEqual(predictions[1].tolist(), expected_predictions[1]) @require_torch_multi_accelerator def test_inference_opt_multi_accelerator(self): processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b") model = Blip2ForConditionalGeneration.from_pretrained( "Salesforce/blip2-opt-2.7b", dtype=torch.float16, device_map="balanced" ) # prepare image image = prepare_img() inputs = processor(images=image, return_tensors="pt").to(0, dtype=torch.float16) predictions = model.generate(**inputs) generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip() # Test output expected_ids = [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118] self.assertEqual(predictions[0].tolist(), [50265] * 32 + expected_ids) # 50265 is the img token id self.assertEqual("a woman sitting on the beach with a dog", generated_text) # image and context prompt = "Question: which city is this? Answer:" inputs = processor(images=image, text=prompt, return_tensors="pt").to(0, dtype=torch.float16) predictions = model.generate(**inputs, max_new_tokens=11) generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip() # Test output expected_ids = [2, 45641, 35, 61, 343, 16, 42, 116, 31652, 35, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118] self.assertEqual(predictions[0].tolist(), [50265] * 32 + expected_ids) # 50265 is the img token id self.assertEqual(generated_text, "Question: which city is this? Answer: it's not a city, it's a beach") @require_torch_multi_accelerator def test_inference_t5_multi_accelerator(self): processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl") device_map = { "query_tokens": 0, "vision_model": 0, "language_model": 1, "language_projection": 0, "qformer": 0, } model = Blip2ForConditionalGeneration.from_pretrained( "Salesforce/blip2-flan-t5-xl", dtype=torch.float16, device_map=device_map ) # prepare image image = prepare_img() inputs = processor(images=image, return_tensors="pt").to(f"{torch_device}:0", dtype=torch.float16) predictions = model.generate(**inputs) generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip() # Test output expected_ids_and_text = Expectations( { ("cuda", None): ( [0, 3, 9, 2335, 19, 1556, 28, 160, 1782, 30, 8, 2608, 1], "a woman is playing with her dog on the beach", ), ("rocm", (9, 5)): ( [0, 3, 9, 2335, 19, 1556, 28, 160, 1782, 30, 8, 2608, 1], "a woman is playing with her dog on the beach", ), } ).get_expectation() self.assertEqual(predictions[0].tolist(), expected_ids_and_text[0]) self.assertEqual(generated_text, expected_ids_and_text[1]) # image and context prompt = "Question: which city is this? Answer:" inputs = processor(images=image, text=prompt, return_tensors="pt").to(f"{torch_device}:0", dtype=torch.float16) predictions = model.generate(**inputs) generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip() # Test output expected_ids_and_text = Expectations( { ("cuda", None): ([0, 3, 7, 152, 2515, 11389, 3523, 1], "san francisco"), ("rocm", (9, 5)): ([0, 3, 7, 152, 2515, 11389, 3523, 1], "san francisco"), } ).get_expectation() self.assertEqual(predictions[0].tolist(), expected_ids_and_text[0]) self.assertEqual(generated_text, expected_ids_and_text[1]) @require_torch_accelerator def test_inference_itm(self): model_name = "Salesforce/blip2-itm-vit-g" processor = Blip2Processor.from_pretrained(model_name) model = Blip2ForImageTextRetrieval.from_pretrained(model_name).to(torch_device) image = prepare_img() text = "A woman and her dog sitting in a beach" inputs = processor(images=image, text=text, return_tensors="pt").to(torch_device) # forward pass out_itm = model(**inputs, use_image_text_matching_head=True) out = model(**inputs) # verify expected_scores = torch.Tensor([[0.0238, 0.9762]]) torch.testing.assert_close(torch.nn.Softmax()(out_itm[0].cpu()), expected_scores, rtol=1e-3, atol=1e-3) torch.testing.assert_close(out[0].cpu(), torch.Tensor([[0.4406]]), rtol=1e-3, atol=1e-3) @require_torch_accelerator @require_torch_fp16 def test_inference_itm_fp16(self): model_name = "Salesforce/blip2-itm-vit-g" processor = Blip2Processor.from_pretrained(model_name) model = Blip2ForImageTextRetrieval.from_pretrained(model_name, dtype=torch.float16).to(torch_device) image = prepare_img() text = "A woman and her dog sitting in a beach" inputs = processor(images=image, text=text, return_tensors="pt").to(torch_device, dtype=torch.float16) # forward pass out_itm = model(**inputs, use_image_text_matching_head=True) out = model(**inputs) # verify expected_scores = torch.Tensor([[0.0239, 0.9761]]) torch.testing.assert_close(torch.nn.Softmax()(out_itm[0].cpu().float()), expected_scores, rtol=1e-3, atol=1e-3) torch.testing.assert_close(out[0].cpu().float(), torch.Tensor([[0.4406]]), rtol=1e-3, atol=1e-3) @require_torch_accelerator @require_torch_fp16 def test_inference_vision_with_projection_fp16(self): model_name = "Salesforce/blip2-itm-vit-g" processor = Blip2Processor.from_pretrained(model_name) model = Blip2VisionModelWithProjection.from_pretrained(model_name, dtype=torch.float16).to(torch_device) image = prepare_img() inputs = processor(images=image, return_tensors="pt").to(torch_device, dtype=torch.float16) # forward pass out = model(**inputs) # verify expected_image_embeds = [ -0.093994140625, -0.075927734375, 0.031890869140625, 0.053009033203125, 0.0352783203125, -0.01190185546875, ] self.assertTrue(np.allclose(out.image_embeds[0][0][:6].tolist(), expected_image_embeds, atol=1e-3)) @require_torch_accelerator @require_torch_fp16 def test_inference_text_with_projection_fp16(self): model_name = "Salesforce/blip2-itm-vit-g" processor = Blip2Processor.from_pretrained(model_name) model = Blip2TextModelWithProjection.from_pretrained(model_name, dtype=torch.float16).to(torch_device) inputs = processor(text="a woman sitting on the beach with a dog", padding=True, return_tensors="pt").to( torch_device ) # forward pass out = model(**inputs) # verify expected_text_embeds = [ -0.1082763671875, 0.053192138671875, -0.02825927734375, 0.0169830322265625, 0.08648681640625, -0.04656982421875, ] self.assertTrue(np.allclose(out.text_embeds[0][0][:6].tolist(), expected_text_embeds, atol=1e-3))