Add SigLIP 2 (#36323)

* Docs * Inits * Auto classes * Add siglip base * Add base tests * Fix Siglip V1 for fix res version * Add image processor * Update conversion * Experimenting with vectorized embeddings * Fixup * Add modular Siglip2Processor * Add modular configuration * Rename num patches * Correct image and text features merging * Working conversion script * Refactoring conversion script * Remove unused code in conversion script * Shorten dict a bit * Refactoring conversion * Done conversion refactoring * Fixup * Modular siglip2 * Make model exportable and compilable without graph breaks * Remove position_ids from image_processor * REmove position ids from modeling file * Update modular * Type hint * Fixup * Set defaults to processor * Add integration test * Revert spatial shapes back to tensor * Change order * Fix most of the tests * Fix docstring * Remove interpolate_pos_encoding arg (not needed) * Update docs * Standardize processing * Fix attention_mask in vision head * Siglip v1: remove double transpose in FA2 * Update modular file * Update FA2 test * Update expected logits * Fix interpolation for siglip2 image processor * Skip init test * Skip dispatch on flash test * Fix modeling tests * Fixup * Add dummy objects * Fix some docstrings * Add siglip2 in index.md * Fix consistency * Add docs * Remove size and data format * Add image processor tests * Fix * Add fast image processor * Fix style * Fix * Docs * Set lowercase for tokenizer * Adjust head size for Siglip v1 * Update siglip2 for consistency with siglip1 * Update siglip2 conversion * Update pipeline * Update checkpoints in tests * Update checkpoint name * Fix pooling for image classification model * Fix FA2 test * Update processor * Fix check repo * Update docs * Fix typos * Fix docstring for fast image processor * Add siglip2 to FA2 docs * Fix fast ip tests * Fix constitency * Fix tokenizer class for siglip v1 * Fix missing header * Refactor scaling for clip, siglip, siglip2 * Remove unused imports * Make fast IP default for siglip2 * Update docs * Update checkpoints * Update modular * Update paper link * Fixup * Fix name in toctree * Fix test
2025-02-21 09:04:19 +00:00
parent 14552cbd7c
commit a957b7911a
33 changed files with 5571 additions and 123 deletions
--- a/tests/models/siglip2/init.py
+++ b/tests/models/siglip2/init.py
--- a/tests/models/siglip2/test_image_processing_siglip2.py
+++ b/tests/models/siglip2/test_image_processing_siglip2.py
@@ -0,0 +1,200 @@
+# coding=utf-8
+# Copyright 2024 HuggingFace Inc.
+#
+# 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.
+
+
+import unittest
+
+import requests
+
+from transformers.testing_utils import require_torch, require_vision
+from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
+
+from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
+
+
+if is_torch_available():
+    import torch
+
+if is_vision_available():
+    from PIL import Image
+
+    from transformers import Siglip2ImageProcessor
+
+
+if is_torchvision_available():
+    from transformers import Siglip2ImageProcessorFast
+
+
+class Siglip2ImageProcessingTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        num_channels=3,
+        image_size=18,
+        min_resolution=30,
+        max_resolution=400,
+        do_resize=True,
+        size=None,
+        do_rescale=True,
+        rescale_factor=1 / 255,
+        do_normalize=True,
+        image_mean=[0.5, 0.5, 0.5],
+        image_std=[0.5, 0.5, 0.5],
+        resample=None,
+        patch_size=16,
+        max_num_patches=256,
+    ):
+        size = size if size is not None else {"height": 18, "width": 18}
+        resample = resample if resample is not None else Image.Resampling.BILINEAR
+        self.parent = parent
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_size = image_size
+        self.min_resolution = min_resolution
+        self.max_resolution = max_resolution
+        self.do_resize = do_resize
+        self.size = size
+        self.do_rescale = do_rescale
+        self.rescale_factor = rescale_factor
+        self.do_normalize = do_normalize
+        self.image_mean = image_mean
+        self.image_std = image_std
+        self.resample = resample
+        self.patch_size = patch_size
+        self.max_num_patches = max_num_patches
+
+    def prepare_image_processor_dict(self):
+        return {
+            "do_resize": self.do_resize,
+            "do_rescale": self.do_rescale,
+            "rescale_factor": self.rescale_factor,
+            "do_normalize": self.do_normalize,
+            "image_mean": self.image_mean,
+            "image_std": self.image_std,
+            "resample": self.resample,
+            "patch_size": self.patch_size,
+            "max_num_patches": self.max_num_patches,
+        }
+
+    def expected_output_image_shape(self, images):
+        return self.max_num_patches, self.patch_size * self.patch_size * self.num_channels
+
+    def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False):
+        return prepare_image_inputs(
+            batch_size=self.batch_size,
+            num_channels=self.num_channels,
+            min_resolution=self.min_resolution,
+            max_resolution=self.max_resolution,
+            equal_resolution=equal_resolution,
+            numpify=numpify,
+            torchify=torchify,
+        )
+
+
+@require_torch
+@require_vision
+# Copied from tests.models.clip.test_image_processing_clip.CLIPImageProcessingTest with CLIP->Siglip2
+class Siglip2ImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
+    image_processing_class = Siglip2ImageProcessor if is_vision_available() else None
+    fast_image_processing_class = Siglip2ImageProcessorFast if is_torchvision_available() else None
+
+    def setUp(self):
+        super().setUp()
+        self.image_processor_tester = Siglip2ImageProcessingTester(self)
+
+    @property
+    def image_processor_dict(self):
+        return self.image_processor_tester.prepare_image_processor_dict()
+
+    # Ignore copy
+    def test_image_processor_properties(self):
+        for image_processing_class in self.image_processor_list:
+            image_processing = image_processing_class(**self.image_processor_dict)
+            self.assertTrue(hasattr(image_processing, "do_resize"))
+            self.assertTrue(hasattr(image_processing, "resample"))
+            self.assertTrue(hasattr(image_processing, "do_rescale"))
+            self.assertTrue(hasattr(image_processing, "rescale_factor"))
+            self.assertTrue(hasattr(image_processing, "do_normalize"))
+            self.assertTrue(hasattr(image_processing, "image_mean"))
+            self.assertTrue(hasattr(image_processing, "image_std"))
+            self.assertTrue(hasattr(image_processing, "patch_size"))
+            self.assertTrue(hasattr(image_processing, "max_num_patches"))
+
+    # Ignore copy
+    def test_image_processor_from_dict_with_kwargs(self):
+        for image_processing_class in self.image_processor_list:
+            image_processor = image_processing_class.from_dict(self.image_processor_dict)
+            self.assertEqual(image_processor.max_num_patches, 256)
+            self.assertEqual(image_processor.patch_size, 16)
+
+            image_processor = self.image_processing_class.from_dict(
+                self.image_processor_dict, patch_size=32, max_num_patches=512
+            )
+            self.assertEqual(image_processor.patch_size, 32)
+            self.assertEqual(image_processor.max_num_patches, 512)
+
+    @unittest.skip(reason="not supported")
+    # Ignore copy
+    def test_call_numpy_4_channels(self):
+        pass
+
+    # increase mean tolerance to 1e-3 -> 2e-3
+    # Ignore copy
+    def test_slow_fast_equivalence(self):
+        if not self.test_slow_image_processor or not self.test_fast_image_processor:
+            self.skipTest(reason="Skipping slow/fast equivalence test")
+
+        if self.image_processing_class is None or self.fast_image_processing_class is None:
+            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
+
+        dummy_image = Image.open(
+            requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
+        )
+        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
+        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
+
+        encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
+        encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
+        torch.testing.assert_close(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1, rtol=1e-1)
+        self.assertLessEqual(
+            torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 2e-3
+        )
+
+    # increase mean tolerance to 1e-3 -> 2e-3
+    # Ignore copy
+    def test_slow_fast_equivalence_batched(self):
+        if not self.test_slow_image_processor or not self.test_fast_image_processor:
+            self.skipTest(reason="Skipping slow/fast equivalence test")
+
+        if self.image_processing_class is None or self.fast_image_processing_class is None:
+            self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
+
+        if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop:
+            self.skipTest(
+                reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors"
+            )
+
+        dummy_images = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
+        image_processor_slow = self.image_processing_class(**self.image_processor_dict)
+        image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
+
+        encoding_slow = image_processor_slow(dummy_images, return_tensors="pt")
+        encoding_fast = image_processor_fast(dummy_images, return_tensors="pt")
+
+        torch.testing.assert_close(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-1, rtol=1e-1)
+        self.assertLessEqual(
+            torch.mean(torch.abs(encoding_slow.pixel_values - encoding_fast.pixel_values)).item(), 2e-3
+        )
--- a/tests/models/siglip2/test_modeling_siglip2.py
+++ b/tests/models/siglip2/test_modeling_siglip2.py
@@ -0,0 +1,989 @@
+# coding=utf-8
+# Copyright 2025 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 Siglip2 model."""
+
+import inspect
+import tempfile
+import unittest
+from typing import Tuple
+
+import numpy as np
+from parameterized import parameterized
+from pytest import mark
+
+from transformers import Siglip2Config, Siglip2TextConfig, Siglip2VisionConfig
+from transformers.testing_utils import (
+    require_flash_attn,
+    require_torch,
+    require_torch_gpu,
+    require_torch_sdpa,
+    require_vision,
+    slow,
+    torch_device,
+)
+from transformers.utils import (
+    is_torch_available,
+    is_torch_bf16_available_on_device,
+    is_torch_fp16_available_on_device,
+    is_torch_sdpa_available,
+    is_vision_available,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+    is_flaky,
+    random_attention_mask,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+
+
+if is_torch_available():
+    import torch
+    from torch import nn
+
+    from transformers import Siglip2ForImageClassification, Siglip2Model, Siglip2TextModel, Siglip2VisionModel
+
+if is_torch_sdpa_available():
+    from torch.nn.attention import SDPBackend, sdpa_kernel
+
+if is_vision_available():
+    from PIL import Image, ImageDraw
+
+    from transformers import Siglip2Processor
+
+
+class Siglip2ModelTesterMixin(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)
+                model_sdpa = model_sdpa.eval().to(torch_device)
+
+                # Load model with eager attention
+                model_eager = model_class.from_pretrained(
+                    tmpdirname,
+                    attn_implementation="eager",
+                )
+                model_eager = model_eager.eval().to(torch_device)
+
+            # SigLip has one shared cls attr for all models, so we assign both submodels heer
+            vision_attn = text_attn = "sdpa" if model._supports_sdpa else "eager"
+
+            if hasattr(model_sdpa, "vision_model") and hasattr(model_sdpa, "text_model"):
+                self.assertTrue(model_sdpa.vision_model.config._attn_implementation == vision_attn)
+                self.assertTrue(model_sdpa.text_model.config._attn_implementation == text_attn)
+                self.assertTrue(model_eager.vision_model.config._attn_implementation == "eager")
+                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")
+
+            for name, submodule in model_eager.named_modules():
+                class_name = submodule.__class__.__name__
+                if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
+                    raise ValueError("The eager model should not have SDPA attention layers")
+
+            has_sdpa = False
+            for name, submodule in model_sdpa.named_modules():
+                class_name = submodule.__class__.__name__
+                if "SdpaAttention" in class_name or "SdpaSelfAttention" in class_name:
+                    has_sdpa = True
+                    break
+            if not has_sdpa and model_sdpa.config.model_type != "falcon":
+                raise ValueError("The SDPA model should have SDPA attention layers")
+
+    def test_eager_matches_sdpa_inference(
+        self,
+        torch_dtype: str,
+        use_attention_mask_options: Tuple[bool, ...] = (True, False),
+        logit_keys: Tuple[str, ...] = ("logits_per_image", "logits_per_text", "image_embeds", "text_embeds"),
+    ):
+        if not self.all_model_classes[0]._supports_sdpa:
+            self.skipTest(f"{self.all_model_classes[0].__name__} does not support SDPA")
+
+        if torch_dtype == "float16" and not is_torch_fp16_available_on_device(torch_device):
+            self.skipTest(f"float16 not supported on {torch_device} (on the specific device currently used)")
+
+        if torch_dtype == "bfloat16" and not is_torch_bf16_available_on_device(torch_device):
+            self.skipTest(
+                f"bfloat16 not supported on {torch_device} (on the specific device currently used, e.g. Nvidia T4 GPU)"
+            )
+
+        # Convert to torch dtype
+        dtypes = {
+            "float16": torch.float16,
+            "bfloat16": torch.bfloat16,
+            "float32": torch.float32,
+        }
+        torch_dtype = dtypes[torch_dtype]
+
+        atols = {
+            torch.float32: 1e-5,
+            torch.bfloat16: 3e-2,
+            torch.float16: 5e-3,
+        }
+        rtols = {
+            torch.float32: 1e-4,
+            torch.bfloat16: 3e-2,
+            torch.float16: 5e-3,
+        }
+
+        atol = atols[torch_dtype]
+        rtol = rtols[torch_dtype]
+
+        def get_mean_reldiff(msg, current_case, x, ref, atol, rtol):
+            return f"{msg} {current_case}: mean relative difference: {((x - ref).abs() / (ref.abs() + 1e-12)).mean():.3e}, torch atol = {atol}, torch rtol = {rtol}"
+
+        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, torch_dtype=torch_dtype)
+                model_sdpa = model_sdpa.eval().to(torch_device)
+
+                # Load model with eager attention
+                model_eager = model_class.from_pretrained(
+                    tmpdirname,
+                    torch_dtype=torch_dtype,
+                    attn_implementation="eager",
+                )
+                model_eager = model_eager.eval().to(torch_device)
+
+            # We use these for loops instead of parameterized.expand just for the interest of avoiding loading/saving the model each time,
+            # but it would be nicer to have an efficient way to use parameterized.expand
+            cases = [
+                (use_mask, output_attentions, sdpa_backend, batch_size)
+                for use_mask in use_attention_mask_options
+                for output_attentions in [True, False]
+                for sdpa_backend in [
+                    SDPBackend.MATH,
+                    [SDPBackend.FLASH_ATTENTION, SDPBackend.MATH],
+                    [SDPBackend.EFFICIENT_ATTENTION, SDPBackend.MATH],
+                    [SDPBackend.FLASH_ATTENTION, SDPBackend.EFFICIENT_ATTENTION, SDPBackend.MATH],
+                ]
+                for batch_size in [1, 5]
+            ]
+            fail_cases = []
+
+            for use_mask, output_attentions, sdpa_backend, batch_size in cases:
+                processed_inputs = inputs_dict.copy()
+
+                # convert to torch_dtype
+                if "pixel_values" in processed_inputs:
+                    processed_inputs["pixel_values"] = processed_inputs["pixel_values"].to(torch_dtype)
+
+                # slice for different batch sizes
+                for key in processed_inputs.keys():
+                    if isinstance(processed_inputs[key], (torch.Tensor, list, tuple)):
+                        processed_inputs[key] = processed_inputs[key][:batch_size]
+
+                # set attention mask with left padding
+                if not use_mask:
+                    processed_inputs.pop("attention_mask", None)
+                else:
+                    dummy_attention_mask = processed_inputs["attention_mask"]
+                    dummy_attention_mask[:] = 1
+                    dummy_attention_mask[:, :1] = 0
+                    processed_inputs["attention_mask"] = dummy_attention_mask
+
+                processed_inputs["output_attentions"] = output_attentions
+                processed_inputs["output_hidden_states"] = True
+
+                current_case = (
+                    f"padding_side=left, use_mask={use_mask}, batch_size={batch_size}, sdpa_backend={sdpa_backend}"
+                )
+
+                prepared_inputs = self._prepare_for_class(processed_inputs, model_class)
+
+                with torch.no_grad():
+                    try:
+                        with sdpa_kernel(sdpa_backend):
+                            outputs_eager = model_eager(**prepared_inputs)
+                            outputs_sdpa = model_sdpa(**prepared_inputs)
+                    except Exception as e:
+                        fail_cases.append(f"{current_case}: {e}")
+                        continue
+
+                for key in logit_keys:
+                    eager_logits = outputs_eager[key]
+                    sdpa_logits = outputs_sdpa[key]
+
+                    if use_mask:
+                        eager_logits = eager_logits[:, 1:]
+                        sdpa_logits = sdpa_logits[:, 1:]
+
+                    is_close = torch.allclose(eager_logits, sdpa_logits, atol=atol, rtol=rtol)
+                    if not is_close:
+                        fail_cases.append(get_mean_reldiff(key, current_case, sdpa_logits, eager_logits, atol, rtol))
+
+            self.assertTrue(len(fail_cases) == 0, "\n".join(fail_cases))
+
+    @require_flash_attn
+    @require_torch_gpu
+    @mark.flash_attn_test
+    @slow
+    def test_flash_attn_2_inference_equivalence(self):
+        dtype = torch.float16
+
+        for model_class in self.all_model_classes:
+            if not model_class._supports_flash_attn_2:
+                self.skipTest(f"{model_class.__name__} does not support Flash Attention 2")
+
+            # Prepare inputs
+            config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+            if "pixel_values" in inputs_dict:
+                inputs_dict["pixel_values"] = inputs_dict["pixel_values"].to(dtype)
+
+            # Separate masks
+            attention_masks = {}
+            if "attention_mask" in inputs_dict:
+                # attention_masks["attention_mask"] = inputs_dict.pop("attention_mask")
+                inputs_dict["attention_mask"] = None
+            if "pixel_attention_mask" in inputs_dict:
+                attention_masks["pixel_attention_mask"] = inputs_dict.pop("pixel_attention_mask")
+                inputs_dict["pixel_attention_mask"] = None
+
+            # Save and load model with flash attention 2 and eager attentions
+            with tempfile.TemporaryDirectory() as tmp_dir:
+                model = model_class(config)
+                model.save_pretrained(tmp_dir)
+
+                model = model_class.from_pretrained(tmp_dir, torch_dtype=dtype)
+                model_fa = model_class.from_pretrained(
+                    tmp_dir, torch_dtype=dtype, attn_implementation="flash_attention_2"
+                )
+
+            model_fa.to(torch_device)
+            model.to(torch_device)
+
+            # Run forward pass without attention masks
+            with torch.no_grad():
+                outputs = model(**inputs_dict, output_hidden_states=True)
+                outputs_fa = model_fa(**inputs_dict, output_hidden_states=True)
+
+            # Choose which key to compare
+            key = [k for k in ["logits", "logits_per_image", "last_hidden_state"] if k in outputs][0]
+
+            torch.testing.assert_close(outputs[key], outputs_fa[key], atol=4e-2, rtol=4e-2)
+
+            # Run forward pass with attention masks
+            inputs_dict.update(attention_masks)
+            with torch.no_grad():
+                outputs = model(**inputs_dict, output_hidden_states=True)
+                outputs_fa = model_fa(**inputs_dict, output_hidden_states=True)
+
+            output_tensor = outputs[key]
+            output_tensor_fa = outputs_fa[key]
+
+            # Mask out padded tokens, they are different for SDPA and Flash Attention 2
+            if key == "last_hidden_state" and "pixel_attention_mask" in inputs_dict:
+                output_tensor = output_tensor * inputs_dict["pixel_attention_mask"][..., None]
+                output_tensor_fa = output_tensor_fa * inputs_dict["pixel_attention_mask"][..., None]
+            elif key == "last_hidden_state" and inputs_dict.get("attention_mask", None) is not None:
+                output_tensor = output_tensor * inputs_dict["attention_mask"][..., None]
+                output_tensor_fa = output_tensor_fa * inputs_dict["attention_mask"][..., None]
+
+            torch.testing.assert_close(output_tensor, output_tensor_fa, atol=4e-2, rtol=4e-2)
+
+            # Check with inference + dropout
+            model.train()
+            _ = model_fa(**inputs_dict, output_hidden_states=True)
+
+    @unittest.skip(reason="Siglip2 has default right padding (tested in test_flash_attn_2_inference_equivalence)")
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        pass
+
+    @unittest.skip(reason="SDPA can't dispatch on flash with not None `attention_mask`")
+    def test_sdpa_can_dispatch_on_flash(self):
+        pass
+
+
+class Siglip2VisionModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        num_patches=16,
+        image_num_patches=24,
+        patch_size=2,
+        num_channels=3,
+        is_training=True,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        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.num_patches = num_patches
+        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
+        self.seq_length = image_num_patches
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor(
+            [self.batch_size, self.seq_length, self.num_channels * self.patch_size * self.patch_size]
+        )
+        pixel_attention_mask = torch.zeros(self.batch_size, self.seq_length, device=torch_device, dtype=torch.long)
+
+        spatial_shapes = [
+            (height, width)
+            for height in range(1, self.seq_length)
+            for width in range(1, self.seq_length)
+            if height * width <= self.seq_length
+        ] * self.batch_size
+        spatial_shapes = spatial_shapes[: self.batch_size]
+        spatial_shapes = torch.tensor(spatial_shapes, device=torch_device, dtype=torch.long)
+
+        for i, (height, width) in enumerate(spatial_shapes):
+            pixel_attention_mask[i, : height * width] = 1
+
+        config = self.get_config()
+
+        return config, pixel_values, pixel_attention_mask, spatial_shapes
+
+    def get_config(self):
+        return Siglip2VisionConfig(
+            num_patches=self.num_patches,
+            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, pixel_attention_mask, spatial_shapes):
+        model = Siglip2VisionModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values, pixel_attention_mask, spatial_shapes)
+
+        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))
+
+    def prepare_config_and_inputs_for_common(self):
+        config, pixel_values, pixel_attention_mask, spatial_shapes = self.prepare_config_and_inputs()
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "pixel_attention_mask": pixel_attention_mask,
+            "spatial_shapes": spatial_shapes,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class Siglip2VisionModelTest(Siglip2ModelTesterMixin, unittest.TestCase):
+    """
+    Here we also overwrite some of the tests of test_modeling_common.py, as SIGLIP2 does not use input_ids, inputs_embeds,
+    attention_mask and seq_length.
+    """
+
+    all_model_classes = (Siglip2VisionModel,) 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 = ["Siglip2MultiheadAttentionPoolingHead"]
+    # 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 = Siglip2VisionModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=Siglip2VisionConfig, has_text_modality=False, hidden_size=37
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(reason="SIGLIP2 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="Siglip2VisionModel does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="Siglip2VisionModel does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="Siglip2VisionModel does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="Siglip2VisionModel does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Siglip2VisionModel has no base class and is not available in MODEL_MAPPING")
+    def test_save_load_fast_init_from_base(self):
+        pass
+
+    @unittest.skip(reason="Siglip2VisionModel has no base class and is not available in MODEL_MAPPING")
+    def test_save_load_fast_init_to_base(self):
+        pass
+
+    @unittest.skip(reason="Siglip2 uses the same initialization scheme as the Flax original implementation")
+    def test_initialization(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/siglip2-base-patch16-naflex"
+        model = Siglip2VisionModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
+    @require_torch_sdpa
+    @slow
+    @is_flaky()
+    def test_eager_matches_sdpa_inference(self, torch_dtype: str):
+        super().test_eager_matches_sdpa_inference(
+            torch_dtype=torch_dtype,
+            logit_keys=("pooler_output", "last_hidden_state"),
+            use_attention_mask_options=(False,),
+        )
+
+    @require_torch_sdpa
+    def test_sdpa_can_dispatch_composite_models(self):
+        super().test_sdpa_can_dispatch_composite_models()
+
+
+class Siglip2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=12,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+        vocab_size=99,
+        hidden_size=32,
+        num_hidden_layers=2,
+        num_attention_heads=4,
+        intermediate_size=37,
+        dropout=0.1,
+        attention_dropout=0.1,
+        max_position_embeddings=512,
+        initializer_range=0.02,
+        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
+
+    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 Siglip2TextConfig(
+            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 = Siglip2TextModel(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))
+
+    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 Siglip2TextModelTest(Siglip2ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (Siglip2TextModel,) if is_torch_available() else ()
+    fx_compatible = False
+    test_resize_embeddings = False
+    test_pruning = False
+    test_head_masking = False
+    model_split_percents = [0.5, 0.8, 0.9]
+
+    def setUp(self):
+        self.model_tester = Siglip2TextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Siglip2TextConfig, hidden_size=37)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    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="Siglip2TextModel does not support standalone training")
+    def test_training(self):
+        pass
+
+    @unittest.skip(reason="Siglip2TextModel does not support standalone training")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="Siglip2TextModel does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="Siglip2TextModel does not support standalone training")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Siglip2 does not use inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="Siglip2TextModel has no base class and is not available in MODEL_MAPPING")
+    def test_save_load_fast_init_from_base(self):
+        pass
+
+    @unittest.skip(reason="Siglip2TextModel has no base class and is not available in MODEL_MAPPING")
+    def test_save_load_fast_init_to_base(self):
+        pass
+
+    @unittest.skip(reason="Siglip2 uses the same initialization scheme as the Flax original implementation")
+    def test_initialization(self):
+        pass
+
+    @slow
+    def test_model_from_pretrained(self):
+        model_name = "google/siglip2-base-patch16-naflex"
+        model = Siglip2TextModel.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
+    @require_torch_sdpa
+    @slow
+    @is_flaky()
+    def test_eager_matches_sdpa_inference(self, torch_dtype: str):
+        super().test_eager_matches_sdpa_inference(
+            torch_dtype=torch_dtype,
+            logit_keys=("pooler_output", "last_hidden_state"),
+            use_attention_mask_options=(False, True),
+        )
+
+    @require_torch_sdpa
+    def test_sdpa_can_dispatch_composite_models(self):
+        super().test_sdpa_can_dispatch_composite_models()
+
+
+class Siglip2ModelTester:
+    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 = Siglip2TextModelTester(parent, **text_kwargs)
+        self.vision_model_tester = Siglip2VisionModelTester(parent, **vision_kwargs)
+        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
+        self.is_training = is_training
+
+    def prepare_config_and_inputs(self):
+        text_config, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        vision_config, pixel_values, pixel_attention_mask, spatial_shapes = (
+            self.vision_model_tester.prepare_config_and_inputs()
+        )
+
+        config = self.get_config()
+
+        return config, input_ids, attention_mask, pixel_values, pixel_attention_mask, spatial_shapes
+
+    def get_config(self):
+        return Siglip2Config.from_text_vision_configs(
+            self.text_model_tester.get_config(),
+            self.vision_model_tester.get_config(),
+        )
+
+    def create_and_check_model(
+        self, config, input_ids, attention_mask, pixel_values, pixel_attention_mask, spatial_shapes
+    ):
+        model = Siglip2Model(config).to(torch_device).eval()
+        with torch.no_grad():
+            result = model(input_ids, pixel_values, pixel_attention_mask, spatial_shapes, 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, pixel_attention_mask, spatial_shapes = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "pixel_values": pixel_values,
+            "pixel_attention_mask": pixel_attention_mask,
+            "spatial_shapes": spatial_shapes,
+            "attention_mask": attention_mask,
+            "position_ids": None,
+            "return_loss": False,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class Siglip2ModelTest(Siglip2ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (Siglip2Model,) if is_torch_available() else ()
+    pipeline_model_mapping = {"feature-extraction": Siglip2Model} 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 = ["Siglip2MultiheadAttentionPoolingHead"]
+    # 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 = Siglip2ModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=Siglip2Config, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    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="Retain_grad is tested in individual model tests")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="Siglip2Model does not have input/output embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Siglip2 uses the same initialization scheme as the Flax original implementation")
+    def test_initialization(self):
+        pass
+
+    def test_load_vision_text_config(self):
+        config, _ = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # Save Siglip2Config and check if we can load Siglip2VisionConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            vision_config = Siglip2VisionConfig.from_pretrained(tmp_dir_name)
+            self.assertDictEqual(config.vision_config.to_dict(), vision_config.to_dict())
+
+        # Save Siglip2Config and check if we can load Siglip2TextConfig from it
+        with tempfile.TemporaryDirectory() as tmp_dir_name:
+            config.save_pretrained(tmp_dir_name)
+            text_config = Siglip2TextConfig.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/siglip2-base-patch16-naflex"
+        model = Siglip2Model.from_pretrained(model_name)
+        self.assertIsNotNone(model)
+
+    @require_flash_attn
+    @require_torch_gpu
+    @mark.flash_attn_test
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        self.skipTest("Siglip2 does not support right padding")
+
+    @parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
+    @require_torch_sdpa
+    @slow
+    @is_flaky()
+    def test_eager_matches_sdpa_inference(self, torch_dtype: str):
+        super().test_eager_matches_sdpa_inference(
+            torch_dtype=torch_dtype,
+            logit_keys=("logits_per_image", "logits_per_text", "image_embeds", "text_embeds"),
+            use_attention_mask_options=(False, True),
+        )
+
+    @require_torch_sdpa
+    def test_sdpa_can_dispatch_composite_models(self):
+        super().test_sdpa_can_dispatch_composite_models()
+
+
+class Siglip2ForImageClassificationModelTester(Siglip2ModelTester):
+    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, pixel_attention_mask, spatial_shapes = self.vision_model_tester.prepare_config_and_inputs()
+        config = self.get_config()
+
+        return config, pixel_values, pixel_attention_mask, spatial_shapes
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values, pixel_attention_mask, spatial_shapes = config_and_inputs
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "pixel_attention_mask": pixel_attention_mask,
+            "spatial_shapes": spatial_shapes,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class Siglip2ForImageClassificationModelTest(Siglip2ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (Siglip2ForImageClassification,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-classification": Siglip2ForImageClassification} 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 = ["Siglip2MultiheadAttentionPoolingHead"]
+    # 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 = Siglip2ForImageClassificationModelTester(self)
+
+    @unittest.skip(reason="Siglip2ForImageClassification does not support inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="Siglip2ForImageClassification does not support inputs_embeds")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(reason="Siglip2ForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="Siglip2ForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="Siglip2ForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Siglip2 uses the same initialization scheme as the Flax original implementation")
+    def test_initialization(self):
+        pass
+
+    @parameterized.expand([("float16",), ("bfloat16",), ("float32",)])
+    @require_torch_sdpa
+    @slow
+    @is_flaky()
+    def test_eager_matches_sdpa_inference(self, torch_dtype: str):
+        super().test_eager_matches_sdpa_inference(
+            torch_dtype=torch_dtype, logit_keys=("logits",), use_attention_mask_options=(False,)
+        )
+
+    @require_torch_sdpa
+    def test_sdpa_can_dispatch_composite_models(self):
+        super().test_sdpa_can_dispatch_composite_models()
+
+
+# Draw a circle on an images with different aspect ratios
+def prepare_images():
+    shapes = [(224, 224), (1024, 1024), (224, 1024)]
+    images = []
+    for height, width in shapes:
+        image = Image.new("RGB", (width, height), color="red")
+        draw = ImageDraw.Draw(image)
+        center_x = image.width // 2
+        center_y = image.height // 2
+        radius = min(center_x, center_y) // 8 * 7
+        draw.ellipse(
+            (center_x - radius, center_y - radius, center_x + radius, center_y + radius),
+            fill="blue",
+            outline="green",
+            width=image.width // 20,
+        )
+        images.append(image)
+    return images
+
+
+@require_vision
+@require_torch
+class Siglip2ModelIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        model_name = "google/siglip2-base-patch16-naflex"
+        model = Siglip2Model.from_pretrained(model_name).to(torch_device)
+        processor = Siglip2Processor.from_pretrained(model_name)
+
+        images = prepare_images()
+        text = [
+            "circle",
+            "ellipsoid",
+            "blue circle on red background",
+            "blue circle with green border on red background",
+            "green circle on red background",
+            "a dog",
+            "a blue dog with a green border on a red background",
+        ]
+
+        inputs = processor(text=text, images=images, return_tensors="pt")
+        inputs = inputs.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 shape
+        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])),
+        )
+
+        # verify the logits values
+        # fmt: off
+        expected_logits_per_text = torch.tensor(
+            [
+                [  1.0195,  -0.0280,  -1.4468],
+                [ -4.5395,  -6.2269,  -1.5667],
+                [  4.1757,   5.0358,   3.5159],
+                [  9.4264,  10.1879,   6.3353],
+                [  2.4409,   3.1058,   4.5491],
+                [-12.3230, -13.7355, -13.4632],
+                [  1.1520,   1.1687,  -1.9647],
+            ]
+        ).to(torch_device)
+        # fmt: on
+
+        torch.testing.assert_close(outputs.logits_per_text, expected_logits_per_text, rtol=1e-3, atol=1e-3)