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tests/models/modernvbert/test_modeling_modernvbert.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 ModernVBERT model."""
+
+import copy
+import unittest
+from typing import ClassVar
+
+from huggingface_hub import hf_hub_download
+from PIL import Image
+
+from transformers import (
+    AutoProcessor,
+    AutoTokenizer,
+    ModernVBertConfig,
+    is_torch_available,
+    is_vision_available,
+)
+from transformers.testing_utils import (
+    cleanup,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        ModernBertConfig,
+        ModernVBertForMaskedLM,
+        ModernVBertForSequenceClassification,
+        ModernVBertForTokenClassification,
+        ModernVBertModel,
+    )
+
+
+if is_vision_available():
+    from PIL import Image
+
+
+class ModernVBertModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=2,
+        num_images=2,
+        text_config=None,
+        is_training=True,
+        vision_config=None,
+        image_token_id: int = 98,
+        pixel_shuffle_factor=2,
+        num_labels=3,
+        use_labels=True,
+        type_sequence_label_size=2,
+    ):
+        if text_config is None:
+            text_config = {
+                "vocab_size": 99,
+                "pad_token_id": 0,
+                "hidden_size": 32,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 4,
+                "intermediate_size": 64,
+                "hidden_activation": "gelu",
+                "mlp_dropout": 0.1,
+                "attention_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,
+            }
+        self.parent = parent
+        self.batch_size = batch_size
+        self.text_config = ModernBertConfig(**text_config)
+        self.vision_config = vision_config
+        self.num_images = num_images
+        self.image_token_id = image_token_id
+        self.image_size = vision_config["image_size"]
+        self.pixel_shuffle_factor = pixel_shuffle_factor
+        self.seq_length = (
+            int(((vision_config["image_size"] // vision_config["patch_size"]) ** 2) / (pixel_shuffle_factor**2))
+            * self.num_images
+        )
+
+        self.vocab_size = text_config["vocab_size"]
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.hidden_size = text_config["hidden_size"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+        self.is_training = is_training
+        self.num_labels = num_labels
+        self.use_labels = use_labels
+        self.type_sequence_label_size = type_sequence_label_size
+
+    def get_config(self):
+        config = ModernVBertConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            image_token_id=self.image_token_id,
+            pixel_shuffle_factor=self.pixel_shuffle_factor,
+            vocab_size=self.vocab_size,
+            attn_implementation={"text_config": "sdpa"},
+        )
+        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])
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
+
+        # For simplicity just set the last n tokens to the image token
+        n_image_tokens_per_batch = self.seq_length
+        input_ids[:, -n_image_tokens_per_batch:] = self.image_token_id
+        attention_mask = input_ids.ne(1).to(torch_device)
+
+        sequence_labels = None
+        token_labels = None
+        if self.use_labels:
+            sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
+            token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
+
+        config = self.get_config()
+
+        # tie text-level args to top-level args for test purposes
+        config.pad_token_id = config.text_config.pad_token_id
+        config.bos_token_id = config.text_config.bos_token_id
+        config.eos_token_id = config.text_config.eos_token_id
+        config.tie_word_embeddings = config.text_config.tie_word_embeddings
+
+        return config, input_ids, attention_mask, pixel_values, sequence_labels, token_labels
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, attention_mask, pixel_values, sequence_labels, token_labels = config_and_inputs
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+    def create_and_check_model(self, config, input_ids, input_mask, pixel_values, sequence_labels, token_labels):
+        model = ModernVBertModel(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+        }
+
+        result = model(**inputs_dict)
+        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
+
+    def create_and_check_for_masked_lm(
+        self, config, input_ids, input_mask, pixel_values, sequence_labels, token_labels
+    ):
+        model = ModernVBertForMaskedLM(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "labels": token_labels,
+        }
+        result = model(**inputs_dict)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
+
+    def create_and_check_for_sequence_classification(
+        self, config, input_ids, input_mask, pixel_values, sequence_labels, token_labels
+    ):
+        config.num_labels = self.num_labels
+        model = ModernVBertForSequenceClassification(config)
+        model.to(torch_device)
+        model.eval()
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "labels": sequence_labels,
+        }
+        result = model(**inputs_dict)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
+
+    def create_and_check_for_token_classification(
+        self, config, input_ids, input_mask, pixel_values, sequence_labels, token_labels
+    ):
+        config.num_labels = self.num_labels
+        model = ModernVBertForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "input_ids": input_ids,
+            "attention_mask": input_mask,
+            "labels": token_labels,
+        }
+        result = model(**inputs_dict)
+        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
+
+
+@require_torch
+class ModernVBertModelTest(ModelTesterMixin, unittest.TestCase):
+    """
+    Model tester for `ModernVBertForMaskedLM`.
+    """
+
+    all_model_classes = (
+        (
+            ModernVBertModel,
+            ModernVBertForMaskedLM,
+            ModernVBertForSequenceClassification,
+            ModernVBertForTokenClassification,
+        )
+        if is_torch_available()
+        else ()
+    )
+    pipeline_model_mapping = (
+        {
+            "feature-extraction": ModernVBertModel,
+            "fill-mask": ModernVBertForMaskedLM,
+            "text-classification": ModernVBertForSequenceClassification,
+            "image-classification": ModernVBertForSequenceClassification,
+            "token-classification": ModernVBertForTokenClassification,
+            "zero-shot": ModernVBertForSequenceClassification,
+        }
+        if is_torch_available()
+        else {}
+    )
+
+    _is_composite = True
+    test_mismatched_shapes = False
+    skip_test_image_features_output_shape = True  # ModernVBert merges batch_size with num_images in index 0
+    model_split_percents = [0.5, 0.8, 0.9]
+
+    def setUp(self):
+        self.model_tester = ModernVBertModelTester(self)
+        self.config_tester = ConfigTester(
+            self,
+            config_class=ModernVBertConfig,
+            has_text_modality=False,  # Avoid the check for vocab_size, which is now in text_config
+            common_properties=None,  # Common properties are now in text_config
+        )
+
+    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)
+
+    def test_for_masked_lm(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
+
+    def test_for_sequence_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
+
+    def test_for_token_classification(self):
+        config_and_inputs = self.model_tester.prepare_config_and_inputs()
+        self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
+
+    # We need to override as we need to prepare such that the image token is the last token
+    def test_resize_tokens_embeddings(self):
+        (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
+
+        for model_class in self.all_model_classes:
+            config = copy.deepcopy(original_config)
+            model = model_class(config)
+            model.to(torch_device)
+
+            if self.model_tester.is_training is False:
+                model.eval()
+
+            model_vocab_size = config.text_config.vocab_size
+            # Retrieve the embeddings and clone theme
+            model_embed = model.resize_token_embeddings(model_vocab_size)
+            cloned_embeddings = model_embed.weight.clone()
+
+            # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
+            model_embed = model.resize_token_embeddings(model_vocab_size + 10)
+            self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
+            # Check that it actually resizes the embeddings matrix
+            self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] + 10)
+
+            # Check that the model can still do a forward pass successfully (every parameter should be resized)
+            model(**self._prepare_for_class(inputs_dict, model_class))
+
+            # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
+            model_embed = model.resize_token_embeddings(model_vocab_size - 15)
+            self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15)
+            # Check that it actually resizes the embeddings matrix
+            self.assertEqual(model_embed.weight.shape[0], cloned_embeddings.shape[0] - 15)
+
+            # Ignore copy
+            # Check that the model can still do a forward pass successfully (every parameter should be resized)
+            # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token
+            inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2)
+            n_images = self.model_tester.num_images * self.model_tester.seq_length
+            model.image_token_id = model_vocab_size - 15 - 1
+            inputs_dict["input_ids"][:, -n_images:] = model.image_token_id
+
+            # make sure that decoder_input_ids are resized as well
+            if "decoder_input_ids" in inputs_dict:
+                inputs_dict["decoder_input_ids"].clamp_(max=model_vocab_size - 15 - 1)
+            model(**self._prepare_for_class(inputs_dict, model_class))
+
+            # Check that adding and removing tokens has not modified the first part of the embedding matrix.
+            models_equal = True
+            for p1, p2 in zip(cloned_embeddings, model_embed.weight):
+                if p1.data.ne(p2.data).sum() > 0:
+                    models_equal = False
+
+            self.assertTrue(models_equal)
+
+            config = copy.deepcopy(original_config)
+            model = model_class(config)
+            model.to(torch_device)
+
+            model_vocab_size = config.text_config.vocab_size
+            model.resize_token_embeddings(model_vocab_size + 10, pad_to_multiple_of=1)
+            self.assertTrue(model.config.text_config.vocab_size + 10, model_vocab_size)
+
+            model_embed = model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=64)
+            self.assertTrue(model_embed.weight.shape[0] // 64, 0)
+
+            self.assertTrue(model_embed.weight.shape[0], model.config.text_config.vocab_size)
+            self.assertTrue(model.config.text_config.vocab_size, model.vocab_size)
+
+            model_embed = model.resize_token_embeddings(model_vocab_size + 13, pad_to_multiple_of=64)
+            self.assertTrue(model_embed.weight.shape[0] // 64, 0)
+
+            # Check that resizing a model to a multiple of pad_to_multiple leads to a model of exactly that size
+            target_dimension = 128
+            model_embed = model.resize_token_embeddings(target_dimension, pad_to_multiple_of=64)
+            self.assertTrue(model_embed.weight.shape[0], target_dimension)
+
+            with self.assertRaisesRegex(
+                ValueError,
+                "Asking to pad the embedding matrix to a multiple of `1.3`, which is not and integer. Please make sure to pass an integer",
+            ):
+                model.resize_token_embeddings(model_vocab_size, pad_to_multiple_of=1.3)
+
+    # We need to override as we need to prepare such that the image token is the last token
+    def test_resize_embeddings_untied(self):
+        (original_config, inputs_dict) = self.model_tester.prepare_config_and_inputs_for_common()
+
+        original_config.tie_word_embeddings = False
+
+        for model_class in self.all_model_classes:
+            config = copy.deepcopy(original_config)
+            model = model_class(config).to(torch_device)
+            model.eval()
+
+            # if no output embeddings -> leave test
+            if model.get_output_embeddings() is None:
+                continue
+
+            # Check that resizing the token embeddings with a larger vocab size increases the model's vocab size
+            model_vocab_size = config.text_config.vocab_size
+            model.resize_token_embeddings(model_vocab_size + 10)
+            self.assertEqual(model.config.text_config.vocab_size, model_vocab_size + 10)
+            output_embeds = model.get_output_embeddings()
+            self.assertEqual(output_embeds.weight.shape[0], model_vocab_size + 10)
+            # Check bias if present
+            if output_embeds.bias is not None:
+                self.assertEqual(output_embeds.bias.shape[0], model_vocab_size + 10)
+            # Check that the model can still do a forward pass successfully (every parameter should be resized)
+            model(**self._prepare_for_class(inputs_dict, model_class))
+
+            # Check that resizing the token embeddings with a smaller vocab size decreases the model's vocab size
+            model.resize_token_embeddings(model_vocab_size - 15)
+            self.assertEqual(model.config.text_config.vocab_size, model_vocab_size - 15)
+            # Check that it actually resizes the embeddings matrix
+            output_embeds = model.get_output_embeddings()
+            self.assertEqual(output_embeds.weight.shape[0], model_vocab_size - 15)
+            # Check bias if present
+            if output_embeds.bias is not None:
+                self.assertEqual(output_embeds.bias.shape[0], model_vocab_size - 15)
+
+            # Check that the model can still do a forward pass successfully (every parameter should be resized)
+            # Input ids should be clamped to the maximum size of the vocabulary - 1 and the image token should be the last token
+            inputs_dict["input_ids"].clamp_(max=model_vocab_size - 15 - 2)
+            n_images = self.model_tester.num_images * self.model_tester.seq_length
+            model.image_token_id = model_vocab_size - 15 - 1
+            inputs_dict["input_ids"][:, -n_images:] = model.image_token_id
+
+            # Check that the model can still do a forward pass successfully (every parameter should be resized)
+            model(**self._prepare_for_class(inputs_dict, model_class))
+
+    @unittest.skip(reason="ModernVBERT model parallelism causes error: self.dtype is broken.")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    @unittest.skip(reason="Vision head's probe has no gradient.")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="Vision head's probe has no gradient.")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Vision head's probe has no gradient.")
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        pass
+
+
+@require_torch
+class ModernVBertForMaskedLMIntegrationTest(unittest.TestCase):
+    model_name: ClassVar[str] = "paultltc/modernvbert_hf"
+
+    def setUp(self):
+        self.torch_dtype = torch.float32
+        self.processor = AutoProcessor.from_pretrained(self.model_name)
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = (
+            ModernVBertForMaskedLM.from_pretrained(self.model_name, torch_dtype=self.torch_dtype)
+            .to(torch_device)
+            .eval()
+        )
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    def test_masked_lm_inference(self):
+        image = Image.open(hf_hub_download("HuggingFaceTB/SmolVLM", "example_images/rococo.jpg", repo_type="space"))
+        text = "This [MASK] is on the wall."
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": text},
+                ],
+            },
+        ]
+
+        prompt = self.processor.apply_chat_template(messages, add_generation_prompt=False)
+        inputs = self.processor(text=prompt, images=[image], return_tensors="pt").to(torch_device)
+
+        with torch.no_grad():
+            outputs = self.model(**inputs)
+
+        masked_index = inputs["input_ids"][0].tolist().index(self.tokenizer.mask_token_id)
+        masked_token_logits = outputs.logits[0, masked_index, :]
+        masked_token_probs = torch.softmax(masked_token_logits, dim=-1)
+        top_5_probs, top_5_indices = torch.topk(masked_token_probs, k=5, dim=-1)
+
+        EXPECTED_TOP_5_INDICES = torch.tensor([13497, 5406, 2460, 22946, 3665], device=torch_device)
+        EXPECTED_TOP_5_VALUES = torch.tensor([0.4986, 0.3550, 0.0415, 0.0235, 0.0199], device=torch_device)
+
+        self.assertTrue(torch.allclose(top_5_indices, EXPECTED_TOP_5_INDICES))
+        self.assertTrue(torch.allclose(top_5_probs, EXPECTED_TOP_5_VALUES, atol=1e-4, rtol=1e-4))