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tests/models/granite4_vision/test_modeling_granite4_vision.py

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+# Copyright 2026 IBM and The HuggingFace 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 Granite4Vision model."""
+
+import unittest
+
+from transformers import (
+    AutoProcessor,
+    CLIPVisionConfig,
+    Granite4VisionConfig,
+    Granite4VisionForConditionalGeneration,
+    Granite4VisionModel,
+    GraniteConfig,
+    is_torch_available,
+)
+from transformers.image_utils import load_image
+from transformers.testing_utils import (
+    Expectations,
+    cleanup,
+    require_deterministic_for_xpu,
+    require_torch,
+    slow,
+    torch_device,
+)
+
+from ...test_modeling_common import floats_tensor
+from ...test_processing_common import url_to_local_path
+from ...vlm_tester import VLMModelTest, VLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+
+class Granite4VisionModelTester(VLMModelTester):
+    base_model_class = Granite4VisionModel
+    config_class = Granite4VisionConfig
+    conditional_generation_class = Granite4VisionForConditionalGeneration
+    text_config_class = GraniteConfig
+    vision_config_class = CLIPVisionConfig
+
+    def __init__(self, parent, **kwargs):
+        # Vision hidden_size must be divisible by 64 (QFormer num_attention_heads = hidden_size // 64)
+        kwargs.setdefault("hidden_size", 64)
+        kwargs.setdefault("intermediate_size", 64)
+        kwargs.setdefault("num_attention_heads", 2)
+        kwargs.setdefault("num_key_value_heads", 2)
+        kwargs.setdefault("num_hidden_layers", 2)
+        # Image/patch sizes: image_side = image_size // patch_size must be divisible by window_side
+        kwargs.setdefault("image_size", 8)
+        kwargs.setdefault("patch_size", 2)
+        kwargs.setdefault("projection_dim", 64)
+        kwargs.setdefault("num_patches_per_image", 2)
+        # Granite4Vision-specific
+        kwargs.setdefault("downsample_rate", "1/2")
+        kwargs.setdefault("deepstack_layer_map", [[1, 0]])
+        kwargs.setdefault("projector_dropout", 0.0)
+        kwargs.setdefault("image_token_index", kwargs.get("image_token_id", 3))
+
+        # Compute num_image_tokens after downsampling:
+        # image_side = image_size/patch_size = 4, ds 1/2 -> patches_h = patches_w = 2
+        # pinpoints [[8,8]] -> scale 1x1 -> current_h = current_w = 2
+        # unpadded = 2*2 = 4, newline = 2, base = 2*2 = 4 -> total = 10
+        kwargs.setdefault("num_image_tokens", 10)
+
+        super().__init__(parent, **kwargs)
+
+    def create_pixel_values(self):
+        """Granite4Vision expects 5D pixel_values: (batch_size, num_patches, channels, height, width)"""
+        return floats_tensor(
+            [
+                self.batch_size,
+                self.num_patches_per_image,
+                self.num_channels,
+                self.image_size,
+                self.image_size,
+            ]
+        )
+
+    def get_additional_inputs(self, config, input_ids, pixel_values):
+        """Granite4Vision requires image_sizes tensor"""
+        return {
+            "image_sizes": torch.tensor([[self.image_size, self.image_size]] * self.batch_size),
+        }
+
+    def get_config(self):
+        config = super().get_config()
+        config.image_grid_pinpoints = [[self.image_size, self.image_size]]
+        config.downsample_rate = self.downsample_rate
+        config.deepstack_layer_map = self.deepstack_layer_map
+        config.projector_dropout = self.projector_dropout
+        config.qformer_config.intermediate_size = 64
+        return config
+
+
+@require_torch
+class Granite4VisionModelTest(VLMModelTest, unittest.TestCase):
+    """
+    Model tester for `Granite4VisionForConditionalGeneration`.
+    """
+
+    model_tester_class = Granite4VisionModelTester
+    skip_test_image_features_output_shape = True
+    test_torch_exportable = False
+    # Custom layer-by-layer forward doesn't support output_attentions
+    # (GraniteDecoderLayer discards attention weights internally)
+    test_attention_outputs = False
+    has_attentions = False
+    test_all_params_have_gradient = False
+
+    @unittest.skip(
+        "VLMs need lots of steps to prepare images/mask correctly to get pad-free inputs. Can be tested as part of LLM test"
+    )
+    def test_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip(
+        "VLMs need lots of steps to prepare images/mask correctly to get pad-free inputs. Can be tested as part of LLM test"
+    )
+    def test_eager_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip("Custom layer-by-layer forward has graph breaks incompatible with fullgraph compile")
+    def test_generate_compile_model_forward_fullgraph(self):
+        pass
+
+    @unittest.skip("Blip2QFormerModel in WindowQFormerDownsampler does not support SDPA dispatch")
+    def test_can_set_attention_dynamically_composite_model(self):
+        pass
+
+
+@require_torch
+class Granite4VisionIntegrationTest(unittest.TestCase):
+    model_id = "ibm-granite/granite-vision-4.1-4b"
+
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained(self.model_id)
+        url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+        self.image = load_image(url_to_local_path(url))
+
+    def make_prompt(self, question):
+        messages = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": question}]}]
+        return self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @require_deterministic_for_xpu
+    @slow
+    def test_small_model_integration_test(self):
+        model = Granite4VisionForConditionalGeneration.from_pretrained(self.model_id, torch_dtype=torch.bfloat16).to(
+            torch_device
+        )
+
+        prompt = self.make_prompt("Describe this image briefly.")
+        inputs = self.processor(text=prompt, images=self.image, return_tensors="pt").to(model.device)
+        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
+        new_tokens = output[:, inputs["input_ids"].shape[1] :]
+
+        EXPECTED_RESPONSE = Expectations({
+            ("cuda", None): "The image depicts two cats resting on a pink couch. They are lying in a relaxed, sprawled position, with one cat appearing to be in a",
+            ("cuda", (8, 6)): "The image depicts two cats resting on a pink blanket. They are lying in a relaxed, sprawled position, with one cat appearing to be in a",
+            ("xpu", None): "The image depicts two cats resting on a pink blanket. They are lying in a relaxed, sprawled position, with one cat appearing to be in a",
+        }).get_expectation()  # fmt: skip
+
+        self.assertEqual(self.processor.decode(new_tokens[0], skip_special_tokens=True), EXPECTED_RESPONSE)
+
+    @require_deterministic_for_xpu
+    @slow
+    def test_small_model_integration_test_batch(self):
+        model = Granite4VisionForConditionalGeneration.from_pretrained(self.model_id, torch_dtype=torch.bfloat16).to(
+            torch_device
+        )
+
+        url2 = "http://images.cocodataset.org/val2017/000000001000.jpg"
+        image2 = load_image(url_to_local_path(url2))
+
+        prompt = self.make_prompt("What do you see in this image?")
+        inputs = self.processor(
+            text=[prompt, prompt],
+            images=[self.image, image2],
+            return_tensors="pt",
+            padding=True,
+        ).to(model.device)
+        output = model.generate(**inputs, max_new_tokens=30, do_sample=False)
+        new_tokens = output[:, inputs["input_ids"].shape[1] :]
+        responses = self.processor.batch_decode(new_tokens, skip_special_tokens=True)
+
+        EXPECTED_RESPONSE = Expectations({
+            ("cuda", (8, 6)): [
+                'i see two cats lying on a pink blanket. one cat is on the left side, and the other is on the right side. there are two',
+                'in the image, i see a group of people, including children and adults, standing on a tennis court. they appear to be posing for a group',
+            ],
+            ("xpu", None): [
+                'i see two cats lying on a pink blanket. one cat is on the left side, and the other is on the right side. there are two',
+                'in the image, i see a group of people, including children and adults, standing on a tennis court. they appear to be posing for a group',
+            ]
+        }).get_expectation()  # fmt: skip
+
+        self.assertEqual(responses[0].lower(), EXPECTED_RESPONSE[0])
+        self.assertEqual(responses[1].lower(), EXPECTED_RESPONSE[1])
+
+    @slow
+    def test_small_model_integration_test_batch_matches_single(self):
+        model = Granite4VisionForConditionalGeneration.from_pretrained(self.model_id, torch_dtype=torch.bfloat16).to(
+            torch_device
+        )
+
+        prompt = self.make_prompt("What do you see in this image?")
+
+        # Single inference
+        inputs_single = self.processor(text=prompt, images=self.image, return_tensors="pt").to(model.device)
+        output_single = model.generate(**inputs_single, max_new_tokens=30, do_sample=False)
+        decoded_single = self.processor.decode(
+            output_single[0, inputs_single["input_ids"].shape[1] :], skip_special_tokens=True
+        )
+
+        # Batch inference (same image as first in batch)
+        url2 = "http://images.cocodataset.org/val2017/000000001000.jpg"
+        image2 = load_image(url_to_local_path(url2))
+        inputs_batch = self.processor(
+            text=[prompt, prompt],
+            images=[self.image, image2],
+            return_tensors="pt",
+            padding=True,
+        ).to(model.device)
+        output_batch = model.generate(**inputs_batch, max_new_tokens=30, do_sample=False)
+        decoded_batch = self.processor.decode(
+            output_batch[0, inputs_batch["input_ids"].shape[1] :], skip_special_tokens=True
+        )
+
+        self.assertEqual(decoded_single, decoded_batch)

tests/models/granite4_vision/test_processing_granite4_vision.py

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+# Copyright 2026 IBM. 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.
+
+import json
+import unittest
+
+import torch
+
+from transformers import Granite4VisionProcessor
+from transformers.testing_utils import require_vision
+
+from ...test_processing_common import ProcessorTesterMixin
+
+
+@require_vision
+class Granite4VisionProcessorTest(ProcessorTesterMixin, unittest.TestCase):
+    processor_class = Granite4VisionProcessor
+    # Image token expansion with downsample_rate="1/2" produces more tokens than the defaults
+    image_text_kwargs_max_length = 300
+    image_text_kwargs_override_max_length = 280
+    image_unstructured_max_length = 260
+
+    @classmethod
+    def _setup_tokenizer(cls):
+        tokenizer_class = cls._get_component_class_from_processor("tokenizer")
+        tokenizer = tokenizer_class.from_pretrained("huggyllama/llama-7b")
+        tokenizer.add_special_tokens({"additional_special_tokens": ["<image>"]})
+        if not tokenizer.pad_token:
+            tokenizer.pad_token = "[PAD]"
+            if tokenizer.pad_token_id is None:
+                tokenizer.pad_token_id = 0
+        return tokenizer
+
+    @classmethod
+    def _setup_test_attributes(cls, processor):
+        cls.image_token = processor.image_token
+
+    @staticmethod
+    def prepare_processor_dict():
+        return {
+            "chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}",
+            "patch_size": 14,
+            "vision_feature_select_strategy": "default",
+            "downsample_rate": "1/2",
+        }  # fmt: skip
+
+    def test_get_num_vision_tokens(self):
+        """Tests general functionality of the helper used internally in vLLM"""
+        processor = self.get_processor()
+
+        output = processor._get_num_multimodal_tokens(image_sizes=[(100, 100), (300, 100), (500, 30)])
+        self.assertTrue("num_image_tokens" in output)
+        self.assertEqual(len(output["num_image_tokens"]), 3)
+
+        self.assertTrue("num_image_patches" in output)
+        self.assertEqual(len(output["num_image_patches"]), 3)
+
+    def test_chat_template_is_saved(self):
+        processor_loaded = self.processor_class.from_pretrained(self.tmpdirname)
+        processor_dict_loaded = json.loads(processor_loaded.to_json_string())
+        # chat templates aren't serialized to json in processors
+        self.assertFalse("chat_template" in processor_dict_loaded)
+
+        # they have to be saved as separate file and loaded back from that file
+        # so we check if the same template is loaded
+        processor_dict = self.prepare_processor_dict()
+        self.assertTrue(processor_loaded.chat_template == processor_dict.get("chat_template", None))
+
+    def test_image_token_filling(self):
+        processor = self.processor_class.from_pretrained(self.tmpdirname)
+        processor.patch_size = 14
+        processor.vision_feature_select_strategy = "default"
+        processor.downsample_rate = "1/2"
+        processor.image_processor.crop_size = {"height": 336, "width": 336}
+        processor.image_processor.size = {"shortest_edge": 336}
+        processor.image_processor.image_grid_pinpoints = [[672, 336]]
+        # Important to check with non square image
+        image = torch.randint(0, 2, (3, 503, 316))
+        image_token_index = processor.image_token_id
+
+        # With downsample_rate="1/2" and patch_size=14:
+        #   patches = 336/14 = 24, after ds: 24*1/2 = 12
+        #   best resolution for (503, 316): [672, 336]
+        #   scale_height=2, scale_width=1
+        #   current = 12*2=24 h, 12*1=12 w
+        #   aspect: 316/503 = 0.628, 12/24 = 0.5 -> orig > current -> new_height = round(503*(12/316)) = 19
+        #   padding = (24-19)//2 = 2, current_height = 24 - 4 = 20
+        #   unpadded = 20*12 = 240, newline = 20
+        #   base = 12*12 + 0 = 144
+        #   total = 240 + 20 + 144 = 404
+        #   with "default" strategy: 404 - 1 = 403
+        expected_image_tokens = 403
+
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image"},
+                    {"type": "text", "text": "What is shown in this image?"},
+                ],
+            },
+        ]
+        inputs = processor(
+            text=[processor.apply_chat_template(messages)],
+            images=[image],
+            return_tensors="pt",
+        )
+        image_tokens = (inputs["input_ids"] == image_token_index).sum().item()
+        self.assertEqual(expected_image_tokens, image_tokens)