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888 lines
38 KiB
Python
888 lines
38 KiB
Python
# 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 InstructBLIP model."""
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import inspect
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import tempfile
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import unittest
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from unittest.mock import patch
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import numpy as np
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import pytest
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import requests
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from transformers import (
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CONFIG_MAPPING,
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BitsAndBytesConfig,
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InstructBlipConfig,
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InstructBlipProcessor,
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InstructBlipQFormerConfig,
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InstructBlipVisionConfig,
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PreTrainedModel,
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)
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from transformers.testing_utils import (
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Expectations,
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cleanup,
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require_accelerate,
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require_bitsandbytes,
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require_flash_attn,
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require_torch,
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require_torch_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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floats_tensor,
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ids_tensor,
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random_attention_mask,
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)
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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 InstructBlipForConditionalGeneration, InstructBlipModel, InstructBlipVisionModel
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if is_vision_available():
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from PIL import Image
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def _prepare_qformer_config_headdim(config, requested_dim):
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config = ModelTesterMixin._prepare_config_headdim(config, requested_dim)
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config.qformer_config.encoder_hidden_size = config.vision_config.hidden_size
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return config
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class InstructBlipVisionModelTester:
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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 case of a vision transformer, 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 InstructBlipVisionConfig(
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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 = InstructBlipVisionModel(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 InstructBlipVisionModelTest(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 InstructBLIP'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 = (InstructBlipVisionModel,) if is_torch_available() else ()
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test_resize_embeddings = False
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def setUp(self):
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self.model_tester = InstructBlipVisionModelTester(self)
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self.config_tester = ConfigTester(
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self,
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config_class=InstructBlipConfig,
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has_text_modality=False,
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common_properties=["num_query_tokens", "image_token_index"],
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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="InstructBLIP'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(reason="This module does not support standalone training")
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def test_training(self):
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pass
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@unittest.skip(reason="This module does not support standalone training")
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def test_training_gradient_checkpointing(self):
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pass
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@unittest.skip(reason="This module does not support standalone training")
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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="This module does not support standalone training")
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def test_training_gradient_checkpointing_use_reentrant_true(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/instructblip-flan-t5-xl"
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model = InstructBlipVisionModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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class InstructBlipQFormerModelTester:
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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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):
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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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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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qformer_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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qformer_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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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, qformer_input_ids, qformer_attention_mask
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def get_config(self):
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return InstructBlipQFormerConfig(
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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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)
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# this class is based on `OPTModelTester` found in tests/models/opt/test_modeling_opt.py
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class InstructBlipTextModelDecoderOnlyTester:
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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=100,
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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 InstructBlipForConditionalGenerationDecoderOnlyModelTester:
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def __init__(
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self,
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parent,
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vision_kwargs=None,
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qformer_kwargs=None,
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text_kwargs=None,
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is_training=True,
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num_query_tokens=10,
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image_token_index=4,
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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 = InstructBlipVisionModelTester(parent, **vision_kwargs)
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self.qformer_model_tester = InstructBlipQFormerModelTester(parent, **qformer_kwargs)
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self.text_model_tester = InstructBlipTextModelDecoderOnlyTester(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 + num_query_tokens # 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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self.image_token_index = image_token_index
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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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_, _, _, qformer_input_ids, qformer_attention_mask = self.qformer_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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vision_tokens = (
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torch.ones((input_ids.shape[0], self.num_query_tokens), device=torch_device, dtype=input_ids.dtype)
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* self.image_token_index
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)
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input_ids[input_ids == self.image_token_index] = self.text_model_tester.pad_token_id
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input_ids = torch.cat([vision_tokens, input_ids], dim=-1)
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vision_attention_mask = torch.ones_like(vision_tokens)
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attention_mask = torch.cat([vision_attention_mask, attention_mask], dim=-1)
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return config, input_ids, attention_mask, qformer_input_ids, qformer_attention_mask, pixel_values
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def get_config(self):
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return InstructBlipConfig(
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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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image_token_index=self.image_token_index,
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)
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def create_and_check_for_conditional_generation(
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self, config, input_ids, attention_mask, qformer_input_ids, qformer_attention_mask, pixel_values
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):
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model = InstructBlipForConditionalGeneration(config).to(torch_device).eval()
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with torch.no_grad():
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result = model(
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pixel_values,
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input_ids=input_ids,
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attention_mask=attention_mask,
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qformer_input_ids=qformer_input_ids,
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qformer_attention_mask=qformer_attention_mask,
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)
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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, qformer_input_ids, qformer_attention_mask, pixel_values = config_and_inputs
|
|
inputs_dict = {
|
|
"pixel_values": pixel_values,
|
|
"input_ids": input_ids,
|
|
"attention_mask": attention_mask,
|
|
"qformer_input_ids": qformer_input_ids,
|
|
"qformer_attention_mask": qformer_attention_mask,
|
|
}
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class InstructBlipForConditionalGenerationDecoderOnlyTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
|
|
all_model_classes = (
|
|
(
|
|
InstructBlipModel,
|
|
InstructBlipForConditionalGeneration,
|
|
)
|
|
if is_torch_available()
|
|
else ()
|
|
)
|
|
pipeline_model_mapping = {"image-text-to-text": InstructBlipForConditionalGeneration}
|
|
additional_model_inputs = ["qformer_input_ids", "input_ids"]
|
|
|
|
test_resize_embeddings = True
|
|
test_attention_outputs = False
|
|
_is_composite = True
|
|
|
|
def setUp(self):
|
|
self.model_tester = InstructBlipForConditionalGenerationDecoderOnlyModelTester(self)
|
|
self.config_tester = ConfigTester(
|
|
self,
|
|
config_class=InstructBlipConfig,
|
|
has_text_modality=False,
|
|
common_properties=["num_query_tokens", "image_token_index"],
|
|
)
|
|
|
|
@staticmethod
|
|
def _prepare_config_headdim(config, requested_dim):
|
|
return _prepare_qformer_config_headdim(config, requested_dim)
|
|
|
|
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="Hidden_states is tested in individual model tests")
|
|
def test_hidden_states_output(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="InstructBlipForConditionalGeneration doesn't support inputs_embeds")
|
|
def test_inputs_embeds(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Tied weights are tested in individual model tests")
|
|
def test_tied_weights_keys(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="Retain_grad is tested in individual model tests")
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="InstructBlipModel does not have input/output embeddings")
|
|
def test_model_get_set_embeddings(self):
|
|
pass
|
|
|
|
@pytest.mark.generate
|
|
@unittest.skip(reason="InstructBlip does not support generation from no inputs")
|
|
def test_generate_without_input_ids(self):
|
|
pass
|
|
|
|
@unittest.skip(reason="InstructBLIP has no separate base model without a head.")
|
|
def test_model_base_model_prefix(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="QFormer is forced to fp32 via _keep_in_fp32_modules, incompatible with SDPA flash-only kernel"
|
|
)
|
|
def test_sdpa_can_dispatch_on_flash(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="QFormer's _keep_in_fp32_modules causes mixed precision incompatible with torch.compile dynamic shapes"
|
|
)
|
|
def test_sdpa_can_compile_dynamic(self):
|
|
pass
|
|
|
|
def flash_attn_inference_equivalence(self, attn_implementation, padding_side, atol=4e-2, rtol=4e-2):
|
|
# The shared helper neither builds `qformer_input_ids` (required by InstructBLIP's Q-Former) nor can read
|
|
# the base `InstructBlipModel`'s nested sub-outputs, so we inject the former and restrict to the generative
|
|
# class.
|
|
_, full_inputs = self.model_tester.prepare_config_and_inputs_for_common()
|
|
qformer_input_ids = full_inputs["qformer_input_ids"]
|
|
base_prepare_for_class = self._prepare_for_class
|
|
|
|
def _prepare_for_class(inputs, model_class, return_labels=False):
|
|
inputs = base_prepare_for_class(inputs, model_class, return_labels=return_labels)
|
|
inputs.setdefault("qformer_input_ids", qformer_input_ids[: inputs[model_class.main_input_name].shape[0]])
|
|
return inputs
|
|
|
|
with (
|
|
patch.object(self, "_prepare_for_class", _prepare_for_class),
|
|
patch.object(self, "all_model_classes", self.all_generative_model_classes),
|
|
):
|
|
super().flash_attn_inference_equivalence(
|
|
attn_implementation=attn_implementation, padding_side=padding_side, atol=atol, rtol=rtol
|
|
)
|
|
|
|
@require_flash_attn
|
|
@require_torch_accelerator
|
|
@require_bitsandbytes
|
|
@pytest.mark.flash_attn_test
|
|
@slow
|
|
def test_flash_attn_2_fp32_ln(self):
|
|
# Overridden to additionally pass `qformer_input_ids`, which InstructBLIP's Q-Former requires.
|
|
if not self.has_attentions:
|
|
self.skipTest(reason="Model architecture does not support attentions")
|
|
|
|
for model_class in self.all_generative_model_classes:
|
|
if not model_class._supports_flash_attn:
|
|
self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
model = model_class(config)
|
|
if not all(
|
|
submodel._supports_flash_attn for submodel in model.modules() if isinstance(submodel, PreTrainedModel)
|
|
):
|
|
self.skipTest(reason="At least some parts of this model do not support flash attention")
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
|
|
pixel_values = inputs_dict[model.main_input_name]
|
|
input_ids = inputs_dict["input_ids"]
|
|
qformer_input_ids = inputs_dict["qformer_input_ids"]
|
|
dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(input_ids))
|
|
batch_size = dummy_attention_mask.shape[0]
|
|
|
|
is_padding_right = dummy_attention_mask[:, -1].sum().item() != batch_size
|
|
if is_padding_right:
|
|
dummy_attention_mask = torch.ones_like(input_ids)
|
|
|
|
model = model_class.from_pretrained(
|
|
tmpdirname,
|
|
dtype=torch.float16,
|
|
attn_implementation="flash_attention_2",
|
|
quantization_config=BitsAndBytesConfig(load_in_4bit=True),
|
|
)
|
|
|
|
for _, param in model.named_parameters():
|
|
if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16):
|
|
param.data = param.data.to(torch.float32)
|
|
|
|
_ = model(pixel_values, input_ids=input_ids, qformer_input_ids=qformer_input_ids)
|
|
_ = model(
|
|
pixel_values,
|
|
input_ids=input_ids,
|
|
attention_mask=dummy_attention_mask,
|
|
qformer_input_ids=qformer_input_ids,
|
|
)
|
|
|
|
@require_flash_attn
|
|
@require_torch_accelerator
|
|
@pytest.mark.flash_attn_test
|
|
@slow
|
|
def test_flash_attn_2_from_config(self):
|
|
# Overridden to additionally pass `qformer_input_ids`, which InstructBLIP's Q-Former requires.
|
|
if not self.has_attentions:
|
|
self.skipTest(reason="Model architecture does not support attentions")
|
|
|
|
for model_class in self.all_generative_model_classes:
|
|
if not model_class._supports_flash_attn:
|
|
self.skipTest(f"{model_class.__name__} does not support flash_attention_2")
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
model = model_class(config)
|
|
if not all(
|
|
submodel._supports_flash_attn for submodel in model.modules() if isinstance(submodel, PreTrainedModel)
|
|
):
|
|
self.skipTest(reason="At least some parts of this model do not support flash_attention_2")
|
|
|
|
fa_model = model_class._from_config(
|
|
config, attn_implementation="flash_attention_2", dtype=torch.bfloat16
|
|
).to(torch_device)
|
|
fa_model = fa_model.train()
|
|
|
|
pixel_values = inputs_dict[fa_model.main_input_name]
|
|
if pixel_values.dtype in [torch.float32, torch.float16]:
|
|
pixel_values = pixel_values.to(torch.bfloat16)
|
|
|
|
input_ids = inputs_dict["input_ids"]
|
|
qformer_input_ids = inputs_dict["qformer_input_ids"]
|
|
dummy_attention_mask = inputs_dict.get("attention_mask", torch.ones_like(input_ids))
|
|
|
|
_ = fa_model(
|
|
pixel_values,
|
|
input_ids=input_ids,
|
|
attention_mask=dummy_attention_mask,
|
|
qformer_input_ids=qformer_input_ids,
|
|
)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
fa_model.save_pretrained(tmpdirname)
|
|
model_from_pretrained = model_class.from_pretrained(tmpdirname)
|
|
self.assertTrue(model_from_pretrained.config._attn_implementation != "flash_attention_2")
|
|
|
|
def test_forward_signature(self):
|
|
config, _ = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
model = model_class(config)
|
|
signature = inspect.signature(model.forward)
|
|
# signature.parameters is an OrderedDict => so arg_names order is deterministic
|
|
arg_names = [*signature.parameters.keys()]
|
|
|
|
expected_arg_names = ["pixel_values"]
|
|
self.assertListEqual(arg_names[:1], expected_arg_names)
|
|
|
|
def test_load_vision_qformer_text_config(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
# Save InstructBlipConfig and check if we can load InstructBlipVisionConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
vision_config = InstructBlipVisionConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
|
|
|
|
# Save InstructBlipConfig and check if we can load InstructBlipQFormerConfig from it
|
|
with tempfile.TemporaryDirectory() as tmp_dir_name:
|
|
config.save_pretrained(tmp_dir_name)
|
|
qformer_config = InstructBlipQFormerConfig.from_pretrained(tmp_dir_name)
|
|
self.assertDictEqual(config.qformer_config.to_dict(), qformer_config.to_dict())
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "Salesforce/instructblip-flan-t5-xl"
|
|
model = InstructBlipForConditionalGeneration.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
# overwrite because InstructBLIP internally calls LM.generate() with embeds thus it cannot operate in no cache format
|
|
def _check_generate_outputs(self, output, config, use_cache=False, num_return_sequences=1, num_beams=1):
|
|
use_cache = True # force this to be True in case False is passed
|
|
super()._check_generate_outputs(
|
|
output, config, use_cache=use_cache, num_return_sequences=num_return_sequences, num_beams=num_beams
|
|
)
|
|
|
|
def test_sdpa_can_dispatch_composite_models(self):
|
|
"""
|
|
Tests if composite models dispatch correctly on SDPA/eager when requested so when loading the model.
|
|
This tests only by looking at layer names, as usually SDPA layers are called "SDPAAttention".
|
|
In contrast to the above test, this one checks if the "config._attn_implementation" is a dict after the model
|
|
is loaded, because we manually replicate requested attn implementation on each sub-config when loading.
|
|
See https://github.com/huggingface/transformers/pull/32238 for more info
|
|
|
|
The test tries to cover most general cases of composite models, VLMs with vision and text configs. Any model
|
|
that has a different set of sub-configs has to overwrite this test.
|
|
"""
|
|
if not self.has_attentions:
|
|
self.skipTest(reason="Model architecture does not support attentions")
|
|
|
|
if not self._is_composite:
|
|
self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
|
|
|
|
for model_class in self.all_model_classes:
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
model = model_class(config)
|
|
|
|
with tempfile.TemporaryDirectory() as tmpdirname:
|
|
model.save_pretrained(tmpdirname)
|
|
model_sdpa = model_class.from_pretrained(tmpdirname)
|
|
model_sdpa = model_sdpa.eval().to(torch_device)
|
|
|
|
# `None` as it is the requested one which will be assigned to each sub-config
|
|
# Sub-model will dispatch to SDPA if it can (checked below that `SDPA` layers are present)
|
|
self.assertTrue(model.language_model.config._attn_implementation == "sdpa")
|
|
self.assertTrue(model.vision_model.config._attn_implementation == "sdpa")
|
|
self.assertTrue(model.qformer.config._attn_implementation == "sdpa")
|
|
|
|
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 _image_features_prepare_config_and_inputs(self):
|
|
"""
|
|
Helper method to extract only image-related inputs from the full set of inputs, for testing `get_image_features`.
|
|
|
|
InstructBlip's `get_image_features` uses `qformer_input_ids` and `qformer_attention_mask` along with `pixel_values`,
|
|
so we override this method to keep those, and only discard `input_ids` and `attention_mask`.
|
|
"""
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
del inputs_dict["input_ids"]
|
|
del inputs_dict["attention_mask"]
|
|
return config, inputs_dict
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
url = "https://huggingface.co/hf-internal-testing/blip-test-image/resolve/main/demo.jpg"
|
|
image = Image.open(requests.get(url, stream=True).raw)
|
|
return image
|
|
|
|
|
|
@require_vision
|
|
@require_torch
|
|
@slow
|
|
class InstructBlipModelIntegrationTest(unittest.TestCase):
|
|
def tearDown(self):
|
|
cleanup(torch_device, gc_collect=False)
|
|
|
|
@require_bitsandbytes
|
|
@require_accelerate
|
|
def test_inference_vicuna_7b(self):
|
|
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-vicuna-7b")
|
|
model = InstructBlipForConditionalGeneration.from_pretrained(
|
|
"Salesforce/instructblip-vicuna-7b",
|
|
quantization_config=BitsAndBytesConfig(load_in_8bit=True),
|
|
attn_implementation="eager",
|
|
)
|
|
|
|
url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
|
|
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
|
|
prompt = "What is unusual about this image?"
|
|
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device, torch.float16)
|
|
|
|
# verify generation
|
|
outputs = model.generate(**inputs, max_new_tokens=30)
|
|
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0].strip()
|
|
|
|
expected_outputs = Expectations(
|
|
{
|
|
("xpu", 3): [32001] * 32 + [2, 1724, 338, 22910, 1048, 445, 1967, 29973, 450, 22910, 9565, 310, 445, 1967, 338, 393, 263, 767, 338, 13977, 292, 22095, 373, 278, 1250, 310, 263, 13328, 20134, 29963, 1550, 19500, 1623, 263, 19587, 4272, 11952, 29889],
|
|
("xpu", 5): [32001] * 32 + [2, 1724, 338, 22910, 1048, 445, 1967, 29973, 450, 22910, 9565, 310, 445, 1967, 338, 393, 263, 767, 338, 13977, 292, 22095, 373, 278, 1250, 310, 263, 13328, 20134, 29963, 1550, 372, 338, 19500, 1623, 263, 19587, 4272],
|
|
("cuda", None): [32001] * 32 + [2, 1724, 338, 22910, 1048, 445, 1967, 29973, 450, 22910, 9565, 310, 445, 1967, 338, 393, 263, 767, 338, 13977, 292, 22095, 373, 278, 1250, 310, 263, 13328, 20134, 29963, 1550, 19500, 373, 263, 19587, 4272, 11952, 29889],
|
|
}
|
|
) # fmt: off
|
|
expected_output = expected_outputs.get_expectation()
|
|
|
|
expected_texts = Expectations(
|
|
{
|
|
("xpu", 3): "What is unusual about this image? The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV while driving down a busy city street.",
|
|
("xpu", 5): "What is unusual about this image? The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV while it is driving down a busy city",
|
|
("cuda", None): "What is unusual about this image? The unusual aspect of this image is that a man is ironing clothes on the back of a yellow SUV while driving on a busy city street.",
|
|
}
|
|
) # fmt: off
|
|
expected_text = expected_texts.get_expectation()
|
|
|
|
self.assertEqual(outputs[0].tolist(), expected_output)
|
|
self.assertEqual(generated_text, expected_text)
|
|
|
|
def test_inference_flant5_xl(self):
|
|
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl")
|
|
model = InstructBlipForConditionalGeneration.from_pretrained(
|
|
"Salesforce/instructblip-flan-t5-xl",
|
|
attn_implementation="eager",
|
|
dtype=torch.bfloat16,
|
|
).to(torch_device)
|
|
|
|
url = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
|
|
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")
|
|
prompt = "What is unusual about this image?"
|
|
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device)
|
|
|
|
for k, v in inputs.items():
|
|
if torch.is_floating_point(v):
|
|
inputs[k] = v.to(torch.bfloat16)
|
|
|
|
outputs = model.generate(
|
|
**inputs,
|
|
do_sample=False,
|
|
num_beams=5,
|
|
max_length=256,
|
|
min_length=1,
|
|
repetition_penalty=1.5,
|
|
length_penalty=1.0,
|
|
temperature=1,
|
|
)
|
|
generated_text = processor.batch_decode(outputs, skip_special_tokens=True)[0]
|
|
|
|
expected_outputs = Expectations(
|
|
{
|
|
(None, None): [0, 37, 1023, 9850, 7, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4459, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 388, 19, 5119, 3, 9, 4459, 8677, 28, 3, 9, 4459, 6177, 6, 11, 3, 88, 19, 3609, 46, 3575, 53, 1476, 16, 80, 609, 11, 3, 9, 10428, 8235, 16, 8, 119, 5, 37, 1023, 19, 7225, 16, 24, 34, 1267, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 1],
|
|
("xpu", 5): [0, 37, 1023, 9850, 7, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4459, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 5, 37, 388, 19, 5119, 3, 9, 4459, 8677, 28, 46, 3575, 53, 1476, 5223, 12, 8, 223, 13, 8, 4049, 6, 15495, 24, 3, 88, 19, 692, 112, 293, 10428, 44, 234, 5, 37, 1023, 19, 7225, 16, 24, 34, 1267, 3, 9, 388, 3575, 53, 4954, 30, 8, 223, 13, 3, 9, 4049, 16, 8, 2214, 13, 3, 9, 3164, 690, 2815, 6, 84, 164, 3130, 24, 3, 88, 19, 692, 112, 293, 10428, 44, 234, 5, 1],
|
|
}
|
|
).get_expectation() # fmt: skip
|
|
self.assertEqual(outputs[0].tolist(), expected_outputs)
|
|
|
|
expected_text = Expectations(
|
|
{
|
|
(None, None): "The image depicts a man ironing clothes on the back of a yellow van in the middle of a busy city street. The man is wearing a yellow shirt with a yellow tie, and he is holding an ironing board in one hand and a laundry basket in the other. The image is unusual in that it shows a man ironing clothes on the back of a van in the middle of a busy city street.",
|
|
("xpu", 5): "The image depicts a man ironing clothes on the back of a yellow van in the middle of a busy city street. The man is wearing a yellow shirt with an ironing board attached to the back of the van, suggesting that he is doing his own laundry at home. The image is unusual in that it shows a man ironing clothes on the back of a van in the middle of a busy city street, which may suggest that he is doing his own laundry at home.",
|
|
}
|
|
).get_expectation() # fmt: skip
|
|
self.assertEqual(generated_text, expected_text)
|
|
|
|
def test_inference_interpolate_pos_encoding(self):
|
|
processor = InstructBlipProcessor.from_pretrained("Salesforce/instructblip-flan-t5-xl")
|
|
model = InstructBlipForConditionalGeneration.from_pretrained(
|
|
"Salesforce/instructblip-flan-t5-xl",
|
|
dtype=torch.bfloat16,
|
|
).to(torch_device)
|
|
processor.image_processor.size = {"height": 500, "width": 500}
|
|
|
|
image = prepare_img()
|
|
prompt = "What's in the image?"
|
|
inputs = processor(images=image, text=prompt, return_tensors="pt").to(torch_device)
|
|
|
|
predictions = model.generate(**inputs, interpolate_pos_encoding=True)
|
|
generated_text = processor.batch_decode(predictions, skip_special_tokens=True)[0].strip()
|
|
|
|
self.assertEqual(
|
|
predictions[0].tolist(), [0, 37, 1023, 753, 3, 9, 2335, 3823, 30, 8, 2608, 28, 3, 9, 1782, 5, 1]
|
|
)
|
|
self.assertEqual(generated_text, "The image features a woman sitting on the beach with a dog.")
|