init

transformers/tests/models/siglip/test_modeling_siglip.py

@@ -0,0 +1,732 @@
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Testing suite for the PyTorch SigLIP model."""
+
+import inspect
+import os
+import tempfile
+import unittest
+
+import numpy as np
+import requests
+from parameterized import parameterized
+
+from transformers import SiglipConfig, SiglipTextConfig, SiglipVisionConfig
+from transformers.testing_utils import (
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
+from transformers.utils import (
+    is_torch_available,
+    is_vision_available,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION,
+    ModelTesterMixin,
+    _config_zero_init,
+    floats_tensor,
+    ids_tensor,
+    random_attention_mask,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import SiglipForImageClassification, SiglipModel, SiglipTextModel, SiglipVisionModel
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import SiglipProcessor
+
+
+class SiglipModelTesterMixin(ModelTesterMixin):
+    def test_sdpa_can_dispatch_composite_models(self):
+        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)
+
+                # Load the model with SDPA
+                model_sdpa = model_class.from_pretrained(tmpdirname)
+
+                # Load model with eager attention
+                model_eager = model_class.from_pretrained(
+                    tmpdirname,
+                    attn_implementation="eager",
+                )
+
+            if hasattr(model_sdpa, "vision_model"):
+                self.assertTrue(model_sdpa.vision_model.config._attn_implementation == "sdpa")
+                self.assertTrue(model_eager.vision_model.config._attn_implementation == "eager")
+
+            if hasattr(model_sdpa, "text_model"):
+                self.assertTrue(model_sdpa.text_model.config._attn_implementation == "sdpa")
+                self.assertTrue(model_eager.text_model.config._attn_implementation == "eager")
+
+            self.assertTrue(model_sdpa.config._attn_implementation == "sdpa")
+            self.assertTrue(model_eager.config._attn_implementation == "eager")
+
+
+class SiglipVisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        image_size=4,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=64,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.image_size = image_size
+        self.patch_size = patch_size
+        self.num_channels = num_channels
+        self.is_training = is_training
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+        # in ViT, the seq length equals the number of patches
+        num_patches = (image_size // patch_size) ** 2
+        self.seq_length = num_patches
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def get_config(self):
+        return SiglipVisionConfig(
+            image_size=self.image_size,
+            patch_size=self.patch_size,
+            num_channels=self.num_channels,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, pixel_values):
+        model = SiglipVisionModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values)
+        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
+        image_size = (self.image_size, self.image_size)
+        patch_size = (self.patch_size, self.patch_size)
+        num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPVisionModelTester.prepare_config_and_inputs_for_common
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class SiglipVisionModelTest(SiglipModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as SIGLIP does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (SiglipVisionModel,) if is_torch_available() else ()
+    fx_compatible = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_head_masking = False
+    # MP works but offload doesn't work when the MultiheadAttention is offloaded
+    # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"]
+    # in the dispatch_model function
+    test_cpu_offload = False
+    test_disk_offload_safetensors = False
+    test_disk_offload_bin = False
+
+    def setUp(self):
+        self.model_tester = SiglipVisionModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=SiglipVisionConfig, has_text_modality=False, hidden_size=37
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="SIGLIP does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    def test_model_get_set_embeddings(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
+            x = model.get_output_embeddings()
+            self.assertTrue(x is None or isinstance(x, nn.Linear))
+
+    def test_forward_signature(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            signature = inspect.signature(model.forward)
+            # signature.parameters is an OrderedDict => so arg_names order is deterministic
+            arg_names = [*signature.parameters.keys()]
+
+            expected_arg_names = ["pixel_values"]
+            self.assertListEqual(arg_names[:1], expected_arg_names)
+
+    def test_model(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_model(*config_and_inputs)
+
+    @unittest.skip(reason="SiglipVisionModel does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="SiglipVisionModel does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="SiglipVisionModel does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="SiglipVisionModel does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Siglip uses a non-standard initialization scheme")
+    def test_initialization(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/siglip-base-patch16-224"
+        model = SiglipVisionModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @parameterized.expand(TEST_EAGER_MATCHES_SDPA_INFERENCE_PARAMETERIZATION)
+    def test_eager_matches_sdpa_inference(self, *args):
+        # adding only flaky decorator here and call the parent test method
+        return getattr(ModelTesterMixin, self._testMethodName)(self)
+
+
+class SiglipTextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=64,
+        num_hidden_layers=2,
+        num_attention_heads=2,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        scope=None,
+    ):
+        self.parent = parent
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.max_position_embeddings = max_position_embeddings
+        self.initializer_range = initializer_range
+        self.scope = scope
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        if input_mask is not None:
+            batch_size, seq_length = input_mask.shape
+            rnd_start_indices = np.random.randint(1, seq_length - 1, size=(batch_size,))
+            for batch_idx, start_index in enumerate(rnd_start_indices):
+                input_mask[batch_idx, :start_index] = 1
+                input_mask[batch_idx, start_index:] = 0
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask
+
+    def get_config(self):
+        return SiglipTextConfig(
+            vocab_size=self.vocab_size,
+            hidden_size=self.hidden_size,
+            num_hidden_layers=self.num_hidden_layers,
+            num_attention_heads=self.num_attention_heads,
+            intermediate_size=self.intermediate_size,
+            dropout=self.dropout,
+            attention_dropout=self.attention_dropout,
+            max_position_embeddings=self.max_position_embeddings,
+            initializer_range=self.initializer_range,
+        )
+
+    def create_and_check_model(self, config, input_ids, input_mask):
+        model = SiglipTextModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(input_ids, attention_mask=input_mask)
+            result = model(input_ids)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTester.prepare_config_and_inputs_for_common
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, input_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+@require_torch
+class SiglipTextModelTest(SiglipModelTesterMixin, unittest.TestCase):
+    all_model_classes = (SiglipTextModel,) if is_torch_available() else ()
+    fx_compatible = False
+    test_pruning = False
+    test_head_masking = False
+    model_split_percents = [0.5, 0.8, 0.9]
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.setUp with CLIP->Siglip
+    def setUp(self):
+        self.model_tester = SiglipTextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=SiglipTextConfig, hidden_size=37)
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_config
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_model
+    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="SiglipTextModel does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="SiglipTextModel does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="SiglipTextModel does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="SiglipTextModel does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Siglip does not use inputs_embeds")
+    # Copied from tests.models.clip.test_modeling_clip.CLIPTextModelTest.test_inputs_embeds
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="Siglip uses a non-standard initialization scheme")
+    def test_initialization(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/siglip-base-patch16-224"
+        model = SiglipTextModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+class SiglipModelTester:
+    def __init__(self, parent, text_kwargs=None, vision_kwargs=None, is_training=True):
+        if text_kwargs is None:
+            text_kwargs = {}
+        if vision_kwargs is None:
+            vision_kwargs = {}
+
+        self.parent = parent
+        self.text_model_tester = SiglipTextModelTester(parent, **text_kwargs)
+        self.vision_model_tester = SiglipVisionModelTester(parent, **vision_kwargs)
+        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
+        self.is_training = is_training
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTester.prepare_config_and_inputs
+    def prepare_config_and_inputs(self):
+        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        vision_config, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+
+        config = self.get_config()
+
+        return config, input_ids, attention_mask, pixel_values
+
+    def get_config(self):
+        return SiglipConfig(
+            text_config=self.text_model_tester.get_config().to_dict(),
+            vision_config=self.vision_model_tester.get_config().to_dict(),
+        )
+
+    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values):
+        model = SiglipModel(config).to(torch_device).eval()
+        with torch.no_grad():
+            result = model(input_ids, pixel_values, attention_mask)
+        self.parent.assertEqual(
+            result.logits_per_image.shape, (self.vision_model_tester.batch_size, self.text_model_tester.batch_size)
+        )
+        self.parent.assertEqual(
+            result.logits_per_text.shape, (self.text_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_loss": False,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class SiglipModelTest(SiglipModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    additional_model_inputs = ["pixel_values"]
+    all_model_classes = (SiglipModel,) if is_torch_available() else ()
+    pipeline_model_mapping = {"feature-extraction": SiglipModel} if is_torch_available() else {}
+    fx_compatible = False
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+    # MP works but offload doesn't work when the MultiheadAttention is offloaded
+    # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"]
+    # in the dispatch_model function
+    test_cpu_offload = False
+    test_disk_offload_safetensors = False
+    test_disk_offload_bin = False
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = SiglipModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=SiglipConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model
+    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")
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_hidden_states_output
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="Inputs_embeds is tested in individual model tests")
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_inputs_embeds
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="Retain_grad is tested in individual model tests")
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_retain_grad_hidden_states_attentions
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="SiglipModel does not have input/output embeddings")
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_model_get_set_embeddings
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Siglip uses a non-standard initialization scheme")
+    def test_initialization(self):
+        pass
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest._create_and_check_torchscript with CLIP->Siglip
+    def _create_and_check_torchscript(self, config, inputs_dict):
+        if not self.test_torchscript:
+            self.skipTest(reason="test_torchscript is set to False")
+
+        configs_no_init = _config_zero_init(config)  # To be sure we have no Nan
+        configs_no_init.torchscript = True
+        configs_no_init.return_dict = False
+        for model_class in self.all_model_classes:
+            model = model_class(config=configs_no_init)
+            model.to(torch_device)
+            model.eval()
+
+            try:
+                input_ids = inputs_dict["input_ids"]
+                pixel_values = inputs_dict["pixel_values"]  # Siglip needs pixel_values
+                traced_model = torch.jit.trace(model, (input_ids, pixel_values))
+            except RuntimeError:
+                self.fail("Couldn't trace module.")
+
+            with tempfile.TemporaryDirectory() as tmp_dir_name:
+                pt_file_name = os.path.join(tmp_dir_name, "traced_model.pt")
+
+                try:
+                    torch.jit.save(traced_model, pt_file_name)
+                except Exception:
+                    self.fail("Couldn't save module.")
+
+                try:
+                    loaded_model = torch.jit.load(pt_file_name)
+                except Exception:
+                    self.fail("Couldn't load module.")
+
+            model.to(torch_device)
+            model.eval()
+
+            loaded_model.to(torch_device)
+            loaded_model.eval()
+
+            model_state_dict = model.state_dict()
+            loaded_model_state_dict = loaded_model.state_dict()
+
+            non_persistent_buffers = {}
+            for key in loaded_model_state_dict:
+                if key not in model_state_dict:
+                    non_persistent_buffers[key] = loaded_model_state_dict[key]
+
+            loaded_model_state_dict = {
+                key: value for key, value in loaded_model_state_dict.items() if key not in non_persistent_buffers
+            }
+
+            self.assertEqual(set(model_state_dict.keys()), set(loaded_model_state_dict.keys()))
+
+            model_buffers = list(model.buffers())
+            for non_persistent_buffer in non_persistent_buffers.values():
+                found_buffer = False
+                for i, model_buffer in enumerate(model_buffers):
+                    if torch.equal(non_persistent_buffer, model_buffer):
+                        found_buffer = True
+                        break
+
+                self.assertTrue(found_buffer)
+                model_buffers.pop(i)
+
+            models_equal = True
+            for layer_name, p1 in model_state_dict.items():
+                p2 = loaded_model_state_dict[layer_name]
+                if p1.data.ne(p2.data).sum() > 0:
+                    models_equal = False
+
+            self.assertTrue(models_equal)
+
+    # Copied from tests.models.clip.test_modeling_clip.CLIPModelTest.test_load_vision_text_config with CLIP->Siglip
+    def test_load_vision_text_config(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # Save SiglipConfig and check if we can load SiglipVisionConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            vision_config = SiglipVisionConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
+
+        # Save SiglipConfig and check if we can load SiglipTextConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            text_config = SiglipTextConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.text_config.to_dict(), text_config.to_dict())
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/siglip-base-patch16-224"
+        model = SiglipModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+
+class SiglipForImageClassificationModelTester(SiglipModelTester):
+    def __init__(self, parent):
+        super().__init__(parent)
+        self.batch_size = self.vision_model_tester.batch_size
+        self.num_hidden_layers = self.vision_model_tester.num_hidden_layers
+        self.hidden_size = self.vision_model_tester.hidden_size
+        self.seq_length = self.vision_model_tester.seq_length
+
+    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
+
+
+@require_torch
+class SiglipForImageClassificationModelTest(SiglipModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (SiglipForImageClassification,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-classification": SiglipForImageClassification} if is_torch_available() else {}
+    fx_compatible = False
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+    # MP works but offload doesn't work when the MultiheadAttention is offloaded
+    # TODO: One potential solution would be to add to set preload_module_classes = ["SiglipMultiheadAttentionPoolingHead"]
+    # in the dispatch_model function
+    test_cpu_offload = False
+    test_disk_offload_safetensors = False
+    test_disk_offload_bin = False
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = SiglipForImageClassificationModelTester(self)
+
+    @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Siglip uses a non-standard initialization scheme")
+    def test_initialization(self):
+        pass
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+    image = Image.open(requests.get(url, stream=True).raw)
+    return image
+
+
+@require_vision
+@require_torch
+class SiglipModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model_name = "google/siglip-base-patch16-224"
+        model = SiglipModel.from_pretrained(model_name).to(torch_device)
+        processor = SiglipProcessor.from_pretrained(model_name)
+
+        image = prepare_img()
+        inputs = processor(
+            text=["a photo of 2 cats", "a photo of 2 dogs"], images=image, padding="max_length", return_tensors="pt"
+        ).to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs)
+            logits_per_image = outputs.logits_per_image
+            logits_per_text = outputs.logits_per_text
+
+        # verify the logits
+        self.assertEqual(
+            logits_per_image.shape,
+            torch.Size((inputs.pixel_values.shape[0], inputs.input_ids.shape[0])),
+        )
+        self.assertEqual(
+            logits_per_text.shape,
+            torch.Size((inputs.input_ids.shape[0], inputs.pixel_values.shape[0])),
+        )
+
+        expected_logits = torch.tensor([[-0.7567, -10.3354]], device=torch_device)
+
+        torch.testing.assert_close(outputs.logits_per_image, expected_logits, rtol=1e-3, atol=1e-3)
+
+        # verify the probs
+        probs = torch.sigmoid(logits_per_image)  # these are the probabilities
+        expected_probs = torch.tensor([[3.1937e-01, 3.2463e-05]], device=torch_device)
+        torch.testing.assert_close(probs, expected_probs, rtol=1e-3, atol=1e-3)
+
+    @slow
+    def test_inference_interpolate_pos_encoding(self):
+        model_name = "google/siglip-base-patch16-224"
+        model = SiglipModel.from_pretrained(model_name).to(torch_device)
+
+        # 640 x 480 image
+        image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+        processor = SiglipProcessor.from_pretrained(model_name, do_resize=False, size={"height": 480, "width": 640})
+
+        inputs = processor(text="what's in the image", images=image, return_tensors="pt").to(torch_device)
+
+        # forward pass
+        with torch.no_grad():
+            outputs = model(**inputs, interpolate_pos_encoding=True)
+
+        # verify the shape
+        # patch size = 16
+        # batch size 1, (640/16) * (480/16) = 1200 patches, 768 hidden size
+        expected_shape = torch.Size((1, 1200, 768))
+
+        self.assertEqual(outputs.vision_model_output.last_hidden_state.shape, expected_shape)