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tests/models/exaone4_5/test_modeling_exaone4_5.py

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+# Copyright 2026 The LG AI Research and 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 EXAONE 4.5 model."""
+
+import copy
+import unittest
+
+from transformers import (
+    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 ...vlm_tester import VLMModelTest, VLMModelTester
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        Exaone4_5_Config,
+        Exaone4_5_ForConditionalGeneration,
+        Exaone4_5_Model,
+        Exaone4_5_Processor,
+        Exaone4_5_VisionConfig,
+        Exaone4Config,
+    )
+
+
+class Exaone4_5_ModelTester(VLMModelTester):
+    base_model_class = Exaone4_5_Model
+    config_class = Exaone4_5_Config
+    text_config_class = Exaone4Config
+    vision_config_class = Exaone4_5_VisionConfig
+    conditional_generation_class = Exaone4_5_ForConditionalGeneration
+
+    def __init__(self, parent, **kwargs):
+        kwargs.setdefault("image_token_id", 3)
+        kwargs.setdefault("video_token_id", 4)
+        kwargs.setdefault("vision_start_token_id", 5)
+        kwargs.setdefault("vision_end_token_id", 6)
+        kwargs.setdefault("image_size", 16)
+        kwargs.setdefault("patch_size", 16)
+        kwargs.setdefault("num_image_tokens", 1)
+        kwargs.setdefault("hidden_act", "silu")
+        kwargs.setdefault("num_attention_heads", 4)
+        kwargs.setdefault("num_key_value_heads", 2)
+        kwargs.setdefault("head_dim", 8)
+        kwargs.setdefault("depth", 2)
+        kwargs.setdefault("num_heads", 4)
+        kwargs.setdefault("spatial_merge_size", 1)
+        kwargs.setdefault("temporal_patch_size", 2)
+        kwargs.setdefault("out_hidden_size", 32)
+        super().__init__(parent, **kwargs)
+
+        # Exaone4_5 vision config expects `in_channels` instead of `num_channels`.
+        self.in_channels = self.num_channels
+
+    def create_pixel_values(self):
+        # EXAONE 4.5 vision tower expects flattened patches:
+        # (total_patches, channels * patch_size^2 * temporal_patch_size)
+        return torch.rand(
+            self.batch_size * (self.image_size**2) // (self.patch_size**2),
+            self.num_channels * (self.patch_size**2) * self.temporal_patch_size,
+            device=torch_device,
+        )
+
+    def get_additional_inputs(self, config, input_ids, pixel_values):
+        return {"image_grid_thw": torch.tensor([[1, 1, 1]] * self.batch_size, device=torch_device)}
+
+    def get_config(self):
+        config = super().get_config()
+        # Some generic generation tests expect these attrs for VLMs.
+        config.vision_start_token_id = self.vision_start_token_id
+        config.vision_end_token_id = self.vision_end_token_id
+        return config
+
+
+@require_torch
+class Exaone4_5_ModelTest(VLMModelTest, unittest.TestCase):
+    model_tester_class = Exaone4_5_ModelTester
+    test_all_params_have_gradient = False
+
+    def test_reverse_loading_mapping(self):
+        super().test_reverse_loading_mapping(skip_base_model=True)
+
+    def test_mismatching_num_image_tokens(self):
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            model = model_class(config).to(torch_device)
+            model.eval()
+            curr_input_dict = copy.deepcopy(input_dict)
+            _ = model(**curr_input_dict)
+
+            # Test 1: fewer images than image placeholders -> should raise.
+            curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-1:, ...]
+            if "image_grid_thw" in curr_input_dict:
+                curr_input_dict["image_grid_thw"] = curr_input_dict["image_grid_thw"][-1:, ...]
+            if "image_sizes" in curr_input_dict:
+                curr_input_dict["image_sizes"] = curr_input_dict["image_sizes"][-1:, ...]
+            with self.assertRaises(ValueError):
+                _ = model(**curr_input_dict)
+
+            # Test 2: one image but two prompts with image placeholders -> should raise.
+            curr_input_dict = {key: val[:1] for key, val in curr_input_dict.items()}
+            for key in ["input_ids", "attention_mask", "token_type_ids"]:
+                if key in curr_input_dict and curr_input_dict[key] is not None:
+                    curr_input_dict[key] = torch.cat([curr_input_dict[key], curr_input_dict[key]], dim=0)
+            with self.assertRaises(ValueError):
+                _ = model(**curr_input_dict)
+
+            # Test 3: two images and two image placeholders -> should pass.
+            curr_input_dict["pixel_values"] = torch.cat(
+                [curr_input_dict["pixel_values"], curr_input_dict["pixel_values"]], dim=0
+            )
+            if "image_grid_thw" in curr_input_dict:
+                curr_input_dict["image_grid_thw"] = torch.cat(
+                    [curr_input_dict["image_grid_thw"], curr_input_dict["image_grid_thw"]], dim=0
+                )
+            if "image_sizes" in curr_input_dict:
+                curr_input_dict["image_sizes"] = torch.cat(
+                    [curr_input_dict["image_sizes"], curr_input_dict["image_sizes"]], dim=0
+                )
+            _ = model(**curr_input_dict)
+
+    @unittest.skip("Model parallel auto-sharding for EXAONE 4.5 VLM is not supported yet.")
+    def test_model_parallelism(self):
+        pass
+
+    @unittest.skip("Beam search with model parallel auto device_map is not stable for EXAONE 4.5 VLM yet.")
+    def test_model_parallel_beam_search(self):
+        pass
+
+
+@require_torch
+class Exaone4_5_IntegrationTest(unittest.TestCase):
+    model_id = "LGAI-EXAONE/EXAONE-4.5-33B"
+    model = None
+    processor = None
+
+    @classmethod
+    def setUpClass(cls):
+        cleanup(torch_device, gc_collect=True)
+        cls.model = Exaone4_5_ForConditionalGeneration.from_pretrained(cls.model_id, device_map="auto")
+        cls.processor = Exaone4_5_Processor.from_pretrained(cls.model_id)
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @require_deterministic_for_xpu
+    @slow
+    def test_model_logits(self):
+        input_ids = [70045, 1109, 115406, 16943, 11697, 115365, 19816, 12137, 375]
+        input_ids = torch.tensor([input_ids]).to(torch_device)
+
+        with torch.no_grad():
+            out = self.model(input_ids).logits.float().cpu()
+
+        EXPECTED_MEAN = Expectations(
+            {
+                ("cuda", (8, 6)): torch.tensor(
+                    [[44.8527, 45.7216, 71.1159, 36.9564, 44.3283, 22.0527, 28.3233, 62.5739, 46.0708]]
+                ),
+                ("xpu", None): torch.tensor(
+                    [[45.2173, 45.4939, 71.0896, 37.1218, 44.3504, 22.1194, 28.6795, 62.5956, 45.9839]]
+                ),
+            }
+        )
+        EXPECTED_SLICE = Expectations(
+            {
+                ("cuda", (8, 6)): torch.tensor(
+                    [42.2500, 43.0000, 42.5000, 44.7500, 49.5000, 46.0000, 46.5000, 46.5000, 45.7500, 46.2500]
+                ),
+                ("xpu", None): torch.tensor(
+                    [42.7500, 43.5000, 42.7500, 45.2500, 50.0000, 46.5000, 46.7500, 46.7500, 46.0000, 46.5000]
+                ),
+            }
+        )
+
+        torch.testing.assert_close(out.mean(-1), EXPECTED_MEAN.get_expectation(), atol=1e-2, rtol=1e-2)
+        torch.testing.assert_close(out[0, 0, :10], EXPECTED_SLICE.get_expectation(), atol=1e-4, rtol=1e-4)
+
+    @require_deterministic_for_xpu
+    @slow
+    def test_model_generation_text_only(self):
+        EXPECTED_TEXT = Expectations(
+            {
+                ("cuda", 8): (
+                    '\nTell me about the Miracle on the Han river.\n\n<think>\n\n</think>\n\nThe **"Miracle on the Han River"**'
+                    " is a term used to describe the rapid economic development and industrialization that South Korea experienced"
+                ),
+                ("xpu", None): (
+                    '\nTell me about the Miracle on the Han river.\n\n<think>\n\n</think>\n\nThe **"Miracle on the Han River"**'
+                    " is a term used to describe the rapid economic development and industrialization that South Korea experienced"
+                ),
+            }
+        )
+        messages = [
+            {"role": "user", "content": [{"type": "text", "text": "Tell me about the Miracle on the Han river."}]}
+        ]
+        input_ids = self.processor.apply_chat_template(
+            messages,
+            tokenize=True,
+            add_generation_prompt=True,
+            return_tensors="pt",
+            enable_thinking=False,
+        ).to(torch_device)
+
+        generated_ids = self.model.generate(input_ids=input_ids, max_new_tokens=20, do_sample=False)
+        text = self.processor.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(text, EXPECTED_TEXT.get_expectation())
+
+    @require_deterministic_for_xpu
+    @slow
+    def test_model_generation_image_text(self):
+        IMAGE_URL = (
+            "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
+        )
+        EXPECTED_TEXT = Expectations(
+            {
+                ("cuda", 8): (
+                    "\n\nDescribe the image.\n\n<think>\n\n</think>\n\nThe image captures a fluffy, young lynx kitten walking across a snowy surface, its thick"
+                ),
+                ("xpu", 3): (
+                    "\n\nDescribe the image.\n\n<think>\n\n</think>\n\nThe image captures a young, fluffy wild cat—likely a lynx kitten—walking through a"
+                ),
+            }
+        )
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg",
+                    },
+                    {"type": "text", "text": "Describe the image."},
+                ],
+            }
+        ]
+        text = self.processor.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+            enable_thinking=False,
+        )
+        image = load_image(IMAGE_URL).convert("RGB")
+
+        inputs = self.processor(text=[text], images=[image], padding=True, return_tensors="pt").to(torch_device)
+        generated_ids = self.model.generate(**inputs, max_new_tokens=20, do_sample=False)
+        text = self.processor.decode(generated_ids[0], skip_special_tokens=True)
+        self.assertEqual(text, EXPECTED_TEXT.get_expectation())