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tests/models/colmodernvbert/test_modeling_colmodernvbert.py

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+# Copyright 2026 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 ColModernVBert model."""
+
+import unittest
+from typing import ClassVar
+
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from tests.test_configuration_common import ConfigTester
+from tests.test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
+from transformers import (
+    is_torch_available,
+)
+from transformers.models.colmodernvbert.configuration_colmodernvbert import ColModernVBertConfig
+from transformers.models.colmodernvbert.modeling_colmodernvbert import (
+    ColModernVBertForRetrieval,
+    ColModernVBertForRetrievalOutput,
+)
+from transformers.models.colmodernvbert.processing_colmodernvbert import ColModernVBertProcessor
+from transformers.testing_utils import (
+    cleanup,
+    require_torch,
+    require_vision,
+    slow,
+    torch_device,
+)
+
+
+if is_torch_available():
+    import torch
+
+
+class ColModernVBertForRetrievalModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        num_images=2,
+        seq_length=7,
+        ignore_index=-100,
+        text_config=None,
+        is_training=False,
+        vision_config=None,
+        pixel_shuffle_factor=2,
+        embedding_dim=64,
+    ):
+        if text_config is None:
+            text_config = {
+                "vocab_size": 99,
+                "pad_token_id": 0,
+                "hidden_size": 32,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 2,
+                "intermediate_size": 64,
+                "hidden_activation": "gelu",
+                "mlp_dropout": 0.1,
+                "embedding_dropout": 0.1,
+                "classifier_dropout": 0.1,
+                "max_position_embeddings": 512,
+                "type_vocab_size": 2,
+                "is_decoder": False,
+                "initializer_range": 0.02,
+                "reference_compile": False,
+            }
+        if vision_config is None:
+            vision_config = {
+                "image_size": 16,
+                "patch_size": 4,
+                "hidden_size": 64,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 4,
+                "intermediate_size": 32,
+                "dropout": 0.1,
+                "attention_dropout": 0.1,
+                "initializer_range": 0.02,
+                "vision_use_head": False,
+            }
+        self.is_training = is_training
+        self.parent = parent
+        self.batch_size = batch_size
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.num_images = num_images
+        self.image_size = vision_config["image_size"]
+        self.pixel_shuffle_factor = pixel_shuffle_factor
+        self.image_token_id = self.text_config["vocab_size"] - 1
+        self.pad_token_id = text_config["pad_token_id"]
+        self.image_seq_length = (
+            int(((vision_config["image_size"] // vision_config["patch_size"]) ** 2) / (pixel_shuffle_factor**2))
+            * self.num_images
+        )
+        self.seq_length = seq_length + self.image_seq_length
+
+        self.hidden_size = text_config["hidden_size"]
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+        self.ignore_index = ignore_index
+
+        self.embedding_dim = embedding_dim
+        self.vlm_config = {
+            "model_type": "modernvbert",
+            "text_config": self.text_config,
+            "vision_config": self.vision_config,
+            "image_token_id": self.image_token_id,
+            "pixel_shuffle_factor": self.pixel_shuffle_factor,
+        }
+
+    def get_config(self):
+        config = ColModernVBertConfig(
+            vlm_config=self.vlm_config,
+            embedding_dim=self.embedding_dim,
+        )
+        return config
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor([self.batch_size, self.num_images, 3, self.image_size, self.image_size])
+        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
+        input_ids = ids_tensor([self.batch_size, self.seq_length], config.vlm_config.text_config.vocab_size)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
+
+        # For simplicity just set the first n tokens to the image token
+        input_ids[input_ids == self.image_token_id] = self.pad_token_id
+        input_ids[:, : self.image_seq_length] = self.image_token_id
+        attention_mask = input_ids.ne(1).to(torch_device)
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class ColModernVBertForRetrievalModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Model tester for `ColModernVBertForRetrieval`.
+    """
+
+    all_model_classes = (ColModernVBertForRetrieval,) if is_torch_available() else ()
+
+    test_resize_embeddings = True
+    test_missing_keys = False
+    model_split_percents = [0.5, 0.8, 0.9]
+
+    def setUp(self):
+        self.model_tester = ColModernVBertForRetrievalModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=ColModernVBertConfig, has_text_modality=False)
+
+    @require_vision
+    def test_colmodernvbert_forward_inputs(self):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            model = model_class(config)
+            model.to(torch_device)
+            model.eval()
+
+            inputs = self._prepare_for_class(inputs_dict, model_class)
+
+            with torch.no_grad():
+                outputs = model(**inputs, return_dict=True)
+
+            self.assertIsInstance(outputs, ColModernVBertForRetrievalOutput)
+
+    @unittest.skip(reason="Error related to ModernBERT model parallelism: self.dtype is broken.")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+
+@require_torch
+class ColModernVBertModelIntegrationTest(unittest.TestCase):
+    model_name: ClassVar[str] = "paultltc/colmodernvbert_hf"
+
+    def setUp(self):
+        self.model_dtype = torch.float32
+        self.processor = ColModernVBertProcessor.from_pretrained(self.model_name)
+        self.model = (
+            ColModernVBertForRetrieval.from_pretrained(
+                self.model_name,
+                dtype=self.model_dtype,
+            )
+            .to(torch_device)
+            .eval()
+        )
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_model_integration_test(self):
+        """
+        Test if the model is able to retrieve the correct pages for a small and easy dataset.
+        """
+        # Load the test dataset
+        queries = [
+            "A paint on the wall",
+            "ColModernVBERT matches the performance of models nearly 10x larger on visual document benchmarks.",
+        ]
+
+        images = [
+            Image.open(hf_hub_download("HuggingFaceTB/SmolVLM", "example_images/rococo.jpg", repo_type="space")),
+            Image.open(hf_hub_download("ModernVBERT/colmodernvbert", "table.png", repo_type="model")),
+        ]
+
+        # Preprocess the examples
+        batch_queries = self.processor.process_queries(text=queries).to(torch_device)
+        batch_images = self.processor.process_images(images=images).to(torch_device)
+
+        # Run inference
+        with torch.inference_mode():
+            image_embeddings = self.model(**batch_images).embeddings
+            query_embeddings = self.model(**batch_queries).embeddings
+
+        # Compute retrieval scores
+        scores = self.processor.score_retrieval(
+            query_embeddings=query_embeddings,
+            passage_embeddings=image_embeddings,
+        )  # (num_queries, num_passages)
+
+        scores = torch.softmax(scores, dim=-1)
+
+        self.assertTrue(scores.ndim == 2, f"Expected 2D tensor, got {scores.ndim}")
+        (
+            self.assertTrue(scores.shape == (len(images), len(images))),
+            (f"Expected shape {(len(images), len(images))}, got {scores.shape}"),
+        )
+
+        # Check if the maximum scores per row are in the diagonal of the matrix score
+        self.assertTrue((scores.argmax(axis=1) == torch.arange(len(images), device=scores.device)).all())
+
+        # Further validation: fine-grained check, with a hardcoded score from the original implementation
+        expected_scores = torch.tensor(
+            [[0.95181, 0.048189], [0.00057251, 0.99943]],
+            dtype=scores.dtype,
+        )
+
+        (
+            self.assertTrue(torch.allclose(scores, expected_scores, atol=1e-2)),
+            f"Expected scores {expected_scores}, got {scores}",
+        )