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7591ca5bc5
* add Blip2ForImageTextRetrieval * use one line and remove unnecessary space in tests Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com> * use value from the config, rather than hardcoded * change order of params in Blip2QFormerModel.forward * update docstring * fix style * update test_inference_opt * move embeddings out of Blip2QFormerModel * remove from_vision_qformer_configs * remove autocast float16 in Blip2QFormerModel * rename fiels into vision_projection,text_projection,use_image_text_matching_head * use CLIPOutput for Blip2ImageTextMatchingModelOutput * remove past_key_values_length from Blip2TextEmbeddings * fix small typo in the CLIPOutput docstring * add Blip2ForImageTextRetrieval to Zero Shot Image Classification mapping * update docstring and add require_torch_fp16 * rollback test_inference_opt * use use_image_text_matching_head=True in convert * skip test_model_get_set_embeddings * fix create_rename_keys error on new itm fields * revert to do scale after dot product between "query" and "key" * fix ValueError on convert script for blip2-opt-2.7b * update org of paths to Salesforce * add is_pipeline_test_to_skip for VisualQuestionAnsweringPipelineTests * [run_slow] blip_2 * removed Blip2ForImageTextRetrieval from IGNORE_NON_AUTO_CONFIGURED * fix docstring of Blip2ImageTextMatchingModelOutput * [run_slow] blip_2 * fix multi-gpu tests * [run_slow] blip_2 * [run_slow] blip_2 --------- Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>
1721 lines
67 KiB
Python
1721 lines
67 KiB
Python
# coding=utf-8
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# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch BLIP-2 model."""
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import inspect
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import tempfile
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import unittest
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import numpy as np
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import requests
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from transformers import CONFIG_MAPPING, Blip2Config, Blip2QFormerConfig, Blip2VisionConfig
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from transformers.testing_utils import (
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require_torch,
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require_torch_fp16,
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require_torch_gpu,
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require_torch_multi_accelerator,
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require_vision,
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slow,
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torch_device,
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)
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from transformers.utils import is_torch_available, is_vision_available
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from ...generation.test_utils import GenerationTesterMixin
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import (
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ModelTesterMixin,
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_config_zero_init,
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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from torch import nn
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from transformers import (
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Blip2ForConditionalGeneration,
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Blip2ForImageTextRetrieval,
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Blip2Model,
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Blip2TextModelWithProjection,
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Blip2VisionModel,
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Blip2VisionModelWithProjection,
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)
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if is_vision_available():
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from PIL import Image
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from transformers import Blip2Processor
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class Blip2VisionModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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hidden_size=32,
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projection_dim=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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initializer_range=1e-10,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.hidden_size = hidden_size
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self.projection_dim = projection_dim
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.initializer_range = initializer_range
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self.scope = scope
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# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
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num_patches = (image_size // patch_size) ** 2
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self.seq_length = num_patches + 1
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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config = self.get_config()
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return config, pixel_values
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def get_config(self):
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return Blip2VisionConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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initializer_range=self.initializer_range,
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)
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def create_and_check_model(self, config, pixel_values):
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model = Blip2VisionModel(config=config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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result = model(pixel_values)
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# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
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image_size = (self.image_size, self.image_size)
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patch_size = (self.patch_size, self.patch_size)
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num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, pixel_values = config_and_inputs
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inputs_dict = {"pixel_values": pixel_values}
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return config, inputs_dict
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@require_torch
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class Blip2VisionModelTest(ModelTesterMixin, unittest.TestCase):
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"""
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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,
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attention_mask and seq_length.
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"""
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all_model_classes = (Blip2VisionModel,) if is_torch_available() else ()
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fx_compatible = False
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test_pruning = False
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test_resize_embeddings = False
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test_head_masking = False
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def setUp(self):
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self.model_tester = Blip2VisionModelTester(self)
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self.config_tester = ConfigTester(
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self, config_class=Blip2VisionConfig, has_text_modality=False, hidden_size=37
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)
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def test_config(self):
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self.config_tester.run_common_tests()
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@unittest.skip(reason="BLIP-2's vision encoder does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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def test_model_get_set_embeddings(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values"]
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self.assertListEqual(arg_names[:1], expected_arg_names)
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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@unittest.skip
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def test_training(self):
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pass
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@unittest.skip
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(reason="Blip2VisionModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_from_base(self):
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pass
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@unittest.skip(reason="Blip2VisionModel has no base class and is not available in MODEL_MAPPING")
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def test_save_load_fast_init_to_base(self):
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pass
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@slow
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def test_model_from_pretrained(self):
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model_name = "Salesforce/blip2-opt-2.7b"
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model = Blip2VisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class Blip2QFormerModelTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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projection_dim=32,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=37,
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dropout=0.1,
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attention_dropout=0.1,
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max_position_embeddings=512,
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initializer_range=0.02,
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bos_token_id=0,
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scope=None,
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use_qformer_text_input=False,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.projection_dim = projection_dim
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.dropout = dropout
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self.attention_dropout = attention_dropout
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self.max_position_embeddings = max_position_embeddings
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self.initializer_range = initializer_range
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self.scope = scope
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self.bos_token_id = bos_token_id
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self.use_qformer_text_input = use_qformer_text_input
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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if input_mask is not None:
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batch_size, seq_length = input_mask.shape
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rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
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for batch_idx, start_index in enumerate(rnd_start_indices):
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input_mask[batch_idx, :start_index] = 1
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input_mask[batch_idx, start_index:] = 0
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config = self.get_config()
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return config, input_ids, input_mask
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def get_config(self):
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return Blip2QFormerConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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projection_dim=self.projection_dim,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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dropout=self.dropout,
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attention_dropout=self.attention_dropout,
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max_position_embeddings=self.max_position_embeddings,
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initializer_range=self.initializer_range,
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bos_token_id=self.bos_token_id,
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use_qformer_text_input=self.use_qformer_text_input,
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)
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# this class is based on `OPTModelTester` found in tests/models/opt/test_modeling_opt.py
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class Blip2TextModelDecoderOnlyTester:
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def __init__(
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self,
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parent,
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batch_size=12,
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seq_length=7,
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is_training=True,
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use_labels=False,
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vocab_size=99,
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hidden_size=16,
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num_hidden_layers=2,
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num_attention_heads=4,
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intermediate_size=4,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=20,
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eos_token_id=2,
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pad_token_id=1,
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bos_token_id=0,
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embed_dim=16,
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num_labels=3,
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word_embed_proj_dim=16,
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type_sequence_label_size=2,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.embed_dim = embed_dim
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self.num_labels = num_labels
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self.type_sequence_label_size = type_sequence_label_size
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self.word_embed_proj_dim = word_embed_proj_dim
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self.is_encoder_decoder = False
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def prepare_config_and_inputs(self):
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config = self.get_config()
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size).clamp(3)
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input_ids[:, -1] = self.eos_token_id # Eos Token
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attention_mask = input_ids.ne(self.pad_token_id)
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return config, input_ids, attention_mask
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def get_config(self):
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return CONFIG_MAPPING["opt"](
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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ffn_dim=self.intermediate_size,
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dropout=self.hidden_dropout_prob,
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attention_dropout=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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eos_token_id=self.eos_token_id,
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bos_token_id=self.bos_token_id,
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pad_token_id=self.pad_token_id,
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embed_dim=self.embed_dim,
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is_encoder_decoder=False,
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word_embed_proj_dim=self.word_embed_proj_dim,
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)
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# this model tester uses a decoder-only language model (OPT)
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class Blip2ForConditionalGenerationDecoderOnlyModelTester:
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def __init__(
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self, parent, vision_kwargs=None, qformer_kwargs=None, text_kwargs=None, is_training=True, num_query_tokens=10
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):
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if vision_kwargs is None:
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vision_kwargs = {}
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if qformer_kwargs is None:
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qformer_kwargs = {}
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if text_kwargs is None:
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text_kwargs = {}
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self.parent = parent
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self.vision_model_tester = Blip2VisionModelTester(parent, **vision_kwargs)
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self.qformer_model_tester = Blip2QFormerModelTester(parent, **qformer_kwargs)
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self.text_model_tester = Blip2TextModelDecoderOnlyTester(parent, **text_kwargs)
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self.batch_size = self.text_model_tester.batch_size # need bs for batching_equivalence test
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self.seq_length = self.text_model_tester.seq_length # need seq_length for common tests
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self.is_training = is_training
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self.num_query_tokens = num_query_tokens
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def prepare_config_and_inputs(self):
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_, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
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_, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
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config = self.get_config()
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return config, input_ids, attention_mask, pixel_values
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def get_config(self):
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return Blip2Config.from_vision_qformer_text_configs(
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vision_config=self.vision_model_tester.get_config(),
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qformer_config=self.qformer_model_tester.get_config(),
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text_config=self.text_model_tester.get_config(),
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num_query_tokens=self.num_query_tokens,
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)
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def create_and_check_for_conditional_generation(self, config, input_ids, attention_mask, pixel_values):
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model = Blip2ForConditionalGeneration(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(pixel_values, input_ids, attention_mask)
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expected_seq_length = self.num_query_tokens + self.text_model_tester.seq_length
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self.parent.assertEqual(
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result.logits.shape,
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(self.vision_model_tester.batch_size, expected_seq_length, self.text_model_tester.vocab_size),
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)
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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config, input_ids, attention_mask, pixel_values = config_and_inputs
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inputs_dict = {
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"pixel_values": pixel_values,
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"input_ids": input_ids,
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"attention_mask": attention_mask,
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"labels": input_ids,
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}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class Blip2ForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
|
all_model_classes = (Blip2ForConditionalGeneration,) if is_torch_available() else ()
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
test_torchscript = False
|
|
|
|
def setUp(self):
|
|
self.model_tester = Blip2ForConditionalGenerationDecoderOnlyModelTester(self)
|
|
|
|
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="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="There's no base Blip2Model")
|
|
def test_save_load_fast_init_from_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="There's no base Blip2Model")
|
|
def test_save_load_fast_init_to_base(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, _ = 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)
|
|
|
|
|
|
# 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,
|
|
)
|
|
|
|
|
|
# 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
|
|
):
|
|
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.is_training = is_training
|
|
self.num_query_tokens = num_query_tokens
|
|
|
|
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.from_vision_qformer_text_configs(
|
|
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,
|
|
)
|
|
|
|
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,
|
|
"labels": labels,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class Blip2ModelTest(ModelTesterMixin, PipelineTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
|
all_model_classes = (Blip2ForConditionalGeneration, Blip2Model) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{
|
|
"feature-extraction": Blip2Model,
|
|
"image-to-text": Blip2ForConditionalGeneration,
|
|
"visual-question-answering": Blip2ForConditionalGeneration,
|
|
}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
test_torchscript = False
|
|
|
|
# TODO: Fix the failed tests
|
|
def is_pipeline_test_to_skip(
|
|
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
|
|
):
|
|
if pipeline_test_casse_name == "VisualQuestionAnsweringPipelineTests":
|
|
# Get `RuntimeError: "LayerNormKernelImpl" not implemented for 'Half'`.
|
|
return True
|
|
|
|
return False
|
|
|
|
def setUp(self):
|
|
self.model_tester = Blip2ModelTester(self)
|
|
|
|
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="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="There's no base Blip2Model")
|
|
def test_save_load_fast_init_from_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="There's no base Blip2Model")
|
|
def test_save_load_fast_init_to_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Does not work on the tiny model as we keep hitting edge cases.")
|
|
def test_cpu_offload(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, _ = 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, (1, 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", "labels"]
|
|
|
|
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,
|
|
(
|
|
self.model_tester.vision_model_tester.batch_size,
|
|
self.model_tester.vision_model_tester.seq_length,
|
|
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", "labels"]
|
|
|
|
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,
|
|
(self.model_tester.vision_model_tester.batch_size, 10, config.vision_config.hidden_size),
|
|
)
|
|
|
|
# override from common to deal with nested configurations (`vision_config`, `text_config` and `qformer_config`)
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
for key in ["vision_config", "qformer_config", "text_config"]:
|
|
setattr(configs_no_init, key, _config_zero_init(getattr(configs_no_init, key)))
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
for name, param in model.named_parameters():
|
|
if param.requires_grad:
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
|
|
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.from_vision_qformer_text_configs(
|
|
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.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 ()
|
|
fx_compatible = False
|
|
test_pruning = False
|
|
test_head_masking = False
|
|
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
test_torchscript = 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
|
|
|
|
@unittest.skip(reason="Blip2TextModelWithProjection has no base class and is not available in MODEL_MAPPING")
|
|
def test_save_load_fast_init_from_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Blip2TextModelWithProjection has no base class and is not available in MODEL_MAPPING")
|
|
def test_save_load_fast_init_to_base(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_gpu
|
|
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.from_vision_qformer_text_configs(
|
|
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.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 ()
|
|
fx_compatible = False
|
|
test_pruning = False
|
|
test_head_masking = False
|
|
|
|
test_resize_embeddings = False
|
|
test_torchscript = 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))
|
|
|
|
@unittest.skip(reason="Blip2VisionModelWithProjection has no base class and is not available in MODEL_MAPPING")
|
|
def test_save_load_fast_init_from_base(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Blip2VisionModelWithProjection has no base class and is not available in MODEL_MAPPING")
|
|
def test_save_load_fast_init_to_base(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[: len(expected_arg_names)], expected_arg_names)
|
|
|
|
@slow
|
|
@require_torch_gpu
|
|
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.from_vision_qformer_text_configs(
|
|
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 ()
|
|
fx_compatible = False
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_resize_embeddings = False
|
|
test_attention_outputs = False
|
|
test_torchscript = 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_gpu
|
|
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
|
|
|
|
def test_initialization(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
configs_no_init = _config_zero_init(config)
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config=configs_no_init)
|
|
for name, param in model.named_parameters():
|
|
if param.requires_grad:
|
|
# check if `logit_scale` is initilized as per the original implementation
|
|
if name == "logit_scale":
|
|
self.assertAlmostEqual(
|
|
param.data.item(),
|
|
np.log(1 / 0.07),
|
|
delta=1e-3,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
elif name == "temp":
|
|
self.assertAlmostEqual(
|
|
param.data.item(),
|
|
0.07,
|
|
delta=1e-3,
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
else:
|
|
self.assertIn(
|
|
((param.data.mean() * 1e9).round() / 1e9).item(),
|
|
[0.0, 1.0],
|
|
msg=f"Parameter {name} of model {model_class} seems not properly initialized",
|
|
)
|
|
|
|
|
|
# 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 test_inference_opt(self):
|
|
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
|
|
model = Blip2ForConditionalGeneration.from_pretrained(
|
|
"Salesforce/blip2-opt-2.7b", torch_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
|
|
self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
|
|
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
|
|
self.assertEqual(
|
|
predictions[0].tolist(),
|
|
[2, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
|
|
)
|
|
self.assertEqual(generated_text, "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", torch_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()
|
|
|
|
self.assertEqual(predictions[0].tolist(), [2, 102, 693, 8, 2335, 15, 5, 4105, 50118])
|
|
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", torch_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)
|
|
self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 69, 2335, 50118])
|
|
self.assertEqual(predictions[1].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 69, 2335, 50118])
|
|
|
|
def test_inference_t5(self):
|
|
processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
|
|
model = Blip2ForConditionalGeneration.from_pretrained(
|
|
"Salesforce/blip2-flan-t5-xl", torch_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
|
|
self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
|
|
self.assertEqual("woman playing with dog on the beach", 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()
|
|
|
|
# Test output
|
|
self.assertEqual(
|
|
predictions[0].tolist(),
|
|
[0, 3, 7, 152, 67, 839, 1],
|
|
)
|
|
self.assertEqual(generated_text, "san diego")
|
|
|
|
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", torch_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)
|
|
self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
|
|
self.assertEqual(predictions[1].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 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", torch_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
|
|
self.assertEqual(predictions[0].tolist(), [2, 102, 693, 2828, 15, 5, 4105, 19, 10, 2335, 50118])
|
|
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
|
|
self.assertEqual(
|
|
predictions[0].tolist(),
|
|
[2, 24, 18, 45, 10, 343, 6, 24, 18, 10, 4105, 50118],
|
|
)
|
|
self.assertEqual(generated_text, "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 = 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", torch_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
|
|
self.assertEqual(predictions[0].tolist(), [0, 2335, 1556, 28, 1782, 30, 8, 2608, 1])
|
|
self.assertEqual("woman playing with dog on the beach", generated_text)
|
|
|
|
# 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
|
|
self.assertEqual(
|
|
predictions[0].tolist(),
|
|
[0, 3, 7, 152, 67, 839, 1],
|
|
)
|
|
self.assertEqual(generated_text, "san diego")
|
|
|
|
def test_expansion_in_processing(self):
|
|
processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
|
|
model = Blip2ForConditionalGeneration.from_pretrained(
|
|
"Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
|
|
).to(torch_device)
|
|
|
|
image = prepare_img()
|
|
prompt = "Question: which city is this? Answer:"
|
|
|
|
# Make sure we will go the legacy path by setting these args to None
|
|
processor.num_query_tokens = None
|
|
model.config.image_token_index = None
|
|
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
|
|
|
|
predictions = model.generate(**inputs, do_sample=False, max_new_tokens=15)
|
|
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
|
|
|
|
# Add args to the config to trigger new logic when inputs are expanded in processing file
|
|
processor.num_query_tokens = model.config.num_query_tokens
|
|
processor.tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
|
|
model.config.image_token_index = len(processor.tokenizer) - 1
|
|
model.resize_token_embeddings(processor.tokenizer.vocab_size, pad_to_multiple_of=64)
|
|
|
|
# Generate again with new inputs
|
|
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, dtype=torch.float16)
|
|
predictions_expanded = model.generate(**inputs, do_sample=False, max_new_tokens=15)
|
|
generated_text_expanded = processor.batch_decode(predictions_expanded, skip_special_tokens=True)[0].strip()
|
|
|
|
self.assertTrue(generated_text_expanded == generated_text)
|
|
|
|
@require_torch_gpu
|
|
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]])
|
|
self.assertTrue(torch.allclose(torch.nn.Softmax()(out_itm[0].cpu()), expected_scores, rtol=1e-3, atol=1e-3))
|
|
self.assertTrue(torch.allclose(out[0].cpu(), torch.Tensor([[0.4406]]), rtol=1e-3, atol=1e-3))
|
|
|
|
@require_torch_gpu
|
|
@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, torch_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]])
|
|
self.assertTrue(
|
|
torch.allclose(torch.nn.Softmax()(out_itm[0].cpu().float()), expected_scores, rtol=1e-3, atol=1e-3)
|
|
)
|
|
self.assertTrue(torch.allclose(out[0].cpu().float(), torch.Tensor([[0.4406]]), rtol=1e-3, atol=1e-3))
|
|
|
|
@require_torch_gpu
|
|
@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, torch_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_gpu
|
|
@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, torch_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))
|