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tests/models/paddleocr_vl/test_modeling_paddleocr_vl.py

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+# 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 PaddleOCRVL model."""
+
+import copy
+import gc
+import unittest
+
+import pytest
+from parameterized import parameterized
+
+from transformers import (
+    AutoProcessor,
+    PaddleOCRVLConfig,
+    PaddleOCRVLForConditionalGeneration,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    backend_empty_cache,
+    require_flash_attn,
+    require_torch,
+    require_torch_accelerator,
+    slow,
+    torch_device,
+)
+
+from ...generation.test_utils import GenerationTesterMixin
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+)
+from ...test_pipeline_mixin import PipelineTesterMixin
+from ...test_processing_common import url_to_local_path
+
+
+if is_torch_available():
+    import torch
+
+
+class PaddleOCRVLVisionText2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        batch_size=7,
+        seq_length=13,
+        num_channels=3,
+        image_height=28,
+        image_width=28,
+        text_config={
+            "pad_token_id": 0,
+            "bos_token_id": 1,
+            "eos_token_id": 2,
+            "vocab_size": 103424,
+            "head_dim": 128,
+            "hidden_act": "silu",
+            "hidden_dropout_prob": 0.0,
+            "hidden_size": 32,
+            "ignored_index": -100,
+            "image_token_id": 100295,
+            "intermediate_size": 32,
+            "max_position_embeddings": 512,
+            "model_type": "paddleocr_vl",
+            "num_attention_heads": 4,
+            "num_hidden_layers": 2,
+            "num_key_value_heads": 2,
+            "rms_norm_eps": 1e-05,
+            "rope_scaling": {"mrope_section": [16, 24, 24], "rope_type": "default", "type": "default"},
+            "rope_theta": 500000,
+            "tie_word_embeddings": False,
+        },
+        vision_start_token_id=101305,
+        vision_end_token_id=101306,
+        image_token_id=100295,
+        is_training=True,
+        vision_config={
+            "hidden_act": "gelu_pytorch_tanh",
+            "hidden_size": 144,
+            "intermediate_size": 32,
+            "layer_norm_eps": 1e-06,
+            "model_type": "paddleocr_vl",
+            "num_attention_heads": 4,
+            "num_channels": 3,
+            "num_hidden_layers": 2,
+            "pad_token_id": 0,
+            "patch_size": 14,
+            "spatial_merge_size": 2,
+        },
+    ):
+        self.parent = parent
+        self.bos_token_id = text_config["bos_token_id"]
+        self.eos_token_id = text_config["eos_token_id"]
+        self.pad_token_id = text_config["pad_token_id"]
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+        self.hidden_size = text_config["hidden_size"]
+        self.vision_start_token_id = vision_start_token_id
+        self.vision_end_token_id = vision_end_token_id
+        self.image_token_id = image_token_id
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.batch_size = batch_size
+        self.num_channels = num_channels
+        self.image_height = image_height
+        self.image_width = image_width
+        self.is_training = is_training
+        self.vocab_size = text_config["vocab_size"]
+        self.num_image_tokens = 1
+        self.seq_length = seq_length + self.num_image_tokens
+
+    def get_config(self):
+        return PaddleOCRVLConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            vision_start_token_id=self.vision_start_token_id,
+            image_token_id=self.image_token_id,
+        )
+
+    def prepare_config_and_inputs(self):
+        config = self.get_config()
+        patch_size = config.vision_config.patch_size
+        pixel_values = floats_tensor(
+            [
+                self.batch_size * (self.image_height * self.image_width) // (patch_size**2),
+                config.vision_config.num_channels,
+                patch_size,
+                patch_size,
+            ]
+        )
+
+        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], self.vocab_size)
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long, device=torch_device)
+
+        input_ids[:, :4] = torch.tensor([100273, 2969, 93963, 93919], dtype=input_ids.dtype, device=input_ids.device)
+        input_ids[:, 4] = self.vision_start_token_id
+        input_ids[:, 5 : 5 + self.num_image_tokens] = self.image_token_id
+        input_ids[:, -8] = self.vision_end_token_id
+        input_ids[:, -7:] = torch.tensor(
+            [93972, 2497, 93963, 23, 92267, 93963, 93919], dtype=input_ids.dtype, device=input_ids.device
+        )
+
+        mm_token_type_ids = torch.zeros_like(input_ids)
+        mm_token_type_ids[input_ids == self.image_token_id] = 1
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "image_grid_thw": torch.tensor([[1, 2, 2]] * self.batch_size, device=torch_device),
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "mm_token_type_ids": mm_token_type_ids,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class PaddleOCRVLModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    """
+    Model tester for `PaddleOCRVLForConditionalGeneration`.
+    """
+
+    all_model_classes = (PaddleOCRVLForConditionalGeneration,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-text-to-text": PaddleOCRVLForConditionalGeneration}
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = PaddleOCRVLVisionText2TextModelTester(self)
+        self.config_tester = ConfigTester(self, config_class=PaddleOCRVLConfig, has_text_modality=False)
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    @unittest.skip(
+        reason="embed_tokens is ~80% of test model size, exceeding the 70% GPU budget so device_map puts everything on CPU"
+    )
+    def test_cpu_offload(self):
+        pass
+
+    @unittest.skip(
+        reason="embed_tokens is ~80% of test model size, exceeding the 70% GPU budget so device_map puts everything on CPU"
+    )
+    def test_disk_offload_bin(self):
+        pass
+
+    @unittest.skip(
+        reason="embed_tokens is ~80% of test model size, exceeding the 70% GPU budget so device_map puts everything on CPU"
+    )
+    def test_disk_offload_safetensors(self):
+        pass
+
+    def test_mismatching_num_image_tokens(self):
+        """
+        Tests that an explicit error is thrown when the number of image tokens
+        doesn't match the number of image placeholders in the text.
+        We also test multi-image cases when one prompt has multiple image tokens.
+        """
+        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)  # in-place modifications further
+            _ = model(**curr_input_dict)  # successful forward with no modifications
+
+            # remove one image but leave all the image tokens in text
+            patch_size = config.vision_config.patch_size
+            one_img_length = (self.model_tester.image_height * self.model_tester.image_width) // (patch_size**2)
+            curr_input_dict["pixel_values"] = curr_input_dict["pixel_values"][-one_img_length:, ...]
+            curr_input_dict["image_grid_thw"] = curr_input_dict["image_grid_thw"][-1:, ...]
+            with self.assertRaisesRegex(ValueError, "Image features and image tokens do not match"):
+                _ = model(**curr_input_dict)
+
+            # simulate multi-image case by concatenating inputs where each has exactly one image/image-token
+            input_ids = curr_input_dict["input_ids"][:1]
+            pixel_values = curr_input_dict["pixel_values"][:one_img_length]
+            image_grid_thw = curr_input_dict["image_grid_thw"][:1]
+            mm_token_type_ids = curr_input_dict["mm_token_type_ids"][:1]
+            input_ids = torch.cat([input_ids, input_ids], dim=0)
+
+            # one image and two image tokens raise an error
+            with self.assertRaisesRegex(ValueError, "Image features and image tokens do not match"):
+                _ = model(
+                    input_ids=input_ids,
+                    pixel_values=pixel_values,
+                    image_grid_thw=image_grid_thw,
+                    mm_token_type_ids=torch.cat([mm_token_type_ids, mm_token_type_ids], dim=0),
+                )
+
+            # two images and two image tokens don't raise an error
+            pixel_values = torch.cat([pixel_values, pixel_values], dim=0)
+            image_grid_thw = torch.cat([image_grid_thw, image_grid_thw], dim=0)
+            mm_token_type_ids = torch.cat([mm_token_type_ids, mm_token_type_ids], dim=0)
+            _ = model(
+                input_ids=input_ids,
+                pixel_values=pixel_values,
+                image_grid_thw=image_grid_thw,
+                mm_token_type_ids=mm_token_type_ids,
+            )
+
+    # PaddleOCRVL has pixel_values shaped as (bs*patch_len, image_channels, patch_size, patch_size) so we can't slice to batches in generate
+    def prepare_config_and_inputs_for_generate(self, batch_size=2):
+        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
+
+        # We don't want a few model inputs in our model input dictionary for generation tests
+        input_keys_to_ignore = [
+            # we don't want encoder-decoder models to start from filled decoder ids
+            "decoder_input_ids",
+            "decoder_attention_mask",
+            # we'll set cache use in each test differently
+            "use_cache",
+            # Ignore labels if it is in the input dict
+            "labels",
+            # model-specific exceptions should overload/overwrite this function
+        ]
+
+        # The diff from the general `prepare_config_and_inputs_for_generate` lies here
+        patch_size = config.vision_config.patch_size
+        filtered_image_length = (
+            batch_size * (self.model_tester.image_height * self.model_tester.image_width) // (patch_size**2)
+        )
+        filtered_inputs_dict = {
+            k: v[:batch_size, ...] if isinstance(v, torch.Tensor) else v
+            for k, v in inputs_dict.items()
+            if k not in input_keys_to_ignore
+        }
+        filtered_inputs_dict["pixel_values"] = inputs_dict["pixel_values"][:filtered_image_length]
+
+        # It is important set `eos_token_id` to `None` to avoid early stopping (would break for length-based checks)
+        text_gen_config = config.get_text_config(decoder=True)
+        if text_gen_config.eos_token_id is not None and text_gen_config.pad_token_id is None:
+            text_gen_config.pad_token_id = (
+                text_gen_config.eos_token_id
+                if isinstance(text_gen_config.eos_token_id, int)
+                else text_gen_config.eos_token_id[0]
+            )
+        text_gen_config.eos_token_id = None
+        text_gen_config.forced_eos_token_id = None
+
+        return config, filtered_inputs_dict
+
+    @unittest.skip(reason="PaddleOCRVL does not support.")
+    def test_generate_compile_model_forward_fullgraph(self):
+        pass
+
+    @unittest.skip(reason="PaddleOCRVL does not support.")
+    def test_multi_gpu_data_parallel_forward(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_sample_generate(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_search_generate(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_search_generate_dict_output(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_search_generate_dict_outputs_use_cache(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_beam_sample_generate_dict_output(self):
+        pass
+
+    @unittest.skip(reason="PaddleOCRVL needs to apply weight conversions.")
+    def test_can_load_from_already_mapped_keys(self):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support beam search.")
+    def test_generate_from_inputs_embeds_1_beam_search(self, _, num_beams):
+        pass
+
+    @parameterized.expand([("random",), ("same",)])
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support assisted decoding.")
+    def test_assisted_decoding_matches_greedy_search(self, assistant_type):
+        pass
+
+    @pytest.mark.generate
+    @unittest.skip(reason="PaddleOCRVL does not support assisted decoding.")
+    def test_assisted_decoding_sample(self):
+        pass
+
+    @unittest.skip("PaddleOCRVL does not support this test.")
+    def test_model_is_small(self):
+        pass
+
+
+@require_torch
+@slow
+class PaddleOCRVLIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained("PaddlePaddle/PaddleOCR-VL")
+        self.messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "url": url_to_local_path(
+                            "https://paddle-model-ecology.bj.bcebos.com/paddlex/imgs/demo_image/ocr_demo2.jpg"
+                        ),
+                    },
+                    {"type": "text", "text": "OCR:"},
+                ],
+            }
+        ]
+
+    def tearDown(self):
+        gc.collect()
+        backend_empty_cache(torch_device)
+
+    def test_small_model_integration_test(self):
+        model = (
+            PaddleOCRVLForConditionalGeneration.from_pretrained(
+                "PaddlePaddle/PaddleOCR-VL",
+                dtype="bfloat16",
+            )
+            .to(torch_device)
+            .eval()
+        )
+
+        inputs = self.processor.apply_chat_template(
+            self.messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+        )
+
+        expected_input_ids_length = 211
+        assert expected_input_ids_length == len(inputs.input_ids[0])
+
+        expected_input_ids = [100273, 2969, 93963, 93919, 101305, 100295, 100295, 100295, 100295, 100295]  # fmt: skip
+        assert expected_input_ids == inputs.input_ids[0].tolist()[:10]
+
+        expected_pixel_slice = torch.tensor(
+            [
+                [1.0000, 1.0000, 1.0000],
+                [1.0000, 1.0000, 1.0000],
+                [0.9922, 0.9922, 0.9922],
+                [1.0000, 1.0000, 1.0000],
+                [1.0000, 1.0000, 1.0000],
+            ],
+            dtype=torch.float32,
+            device="cpu",
+        )
+
+        assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:5, :, 0, 0], atol=3e-3)
+
+        # verify generation
+        inputs = inputs.to(torch_device)
+        output = model.generate(**inputs, max_new_tokens=30)
+        result = self.processor.decode(output[0][inputs["input_ids"].shape[-1] : -1])
+
+        EXPECTED_DECODED_TEXT = "生甘草"
+
+        self.assertEqual(
+            result,
+            EXPECTED_DECODED_TEXT,
+        )
+
+    def test_small_model_integration_test_batch(self):
+        model = (
+            PaddleOCRVLForConditionalGeneration.from_pretrained("PaddlePaddle/PaddleOCR-VL", dtype="bfloat16")
+            .to(torch_device)
+            .eval()
+        )
+
+        inputs = self.processor.apply_chat_template(
+            [self.messages, self.messages],
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            padding_side="left",
+        ).to(torch_device)
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+        generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, output)]
+        result = self.processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        EXPECTED_DECODED_TEXT = ["生甘草", "生甘草"]
+
+        self.assertEqual(
+            result,
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    def test_small_model_integration_test_flashatt2(self):
+        model = (
+            PaddleOCRVLForConditionalGeneration.from_pretrained(
+                "PaddlePaddle/PaddleOCR-VL", dtype="bfloat16", attn_implementation="flash_attention_2"
+            )
+            .to(torch_device)
+            .eval()
+        )
+
+        inputs = self.processor.apply_chat_template(
+            self.messages,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+        )
+
+        expected_input_ids_length = 211
+        assert expected_input_ids_length == len(inputs.input_ids[0])
+
+        expected_input_ids = [100273, 2969, 93963, 93919, 101305, 100295, 100295, 100295, 100295, 100295]  # fmt: skip
+        assert expected_input_ids == inputs.input_ids[0].tolist()[:10]
+
+        expected_pixel_slice = torch.tensor(
+            [
+                [1.0000, 1.0000, 1.0000],
+                [1.0000, 1.0000, 1.0000],
+                [0.9922, 0.9922, 0.9922],
+                [1.0000, 1.0000, 1.0000],
+                [1.0000, 1.0000, 1.0000],
+            ],
+            dtype=torch.float32,
+            device="cpu",
+        )
+        assert torch.allclose(expected_pixel_slice, inputs.pixel_values[:5, :, 0, 0], atol=3e-3)
+
+        # verify generation
+        inputs = inputs.to(torch_device)
+        output = model.generate(**inputs, max_new_tokens=30)
+        result = self.processor.decode(output[0][inputs["input_ids"].shape[-1] : -1])
+
+        EXPECTED_DECODED_TEXT = "生甘草"
+
+        self.assertEqual(
+            result,
+            EXPECTED_DECODED_TEXT,
+        )
+
+    @require_flash_attn
+    @require_torch_accelerator
+    @pytest.mark.flash_attn_test
+    def test_small_model_integration_test_batch_flashatt2(self):
+        model = (
+            PaddleOCRVLForConditionalGeneration.from_pretrained(
+                "PaddlePaddle/PaddleOCR-VL", dtype="bfloat16", attn_implementation="flash_attention_2"
+            )
+            .to(torch_device)
+            .eval()
+        )
+
+        inputs = self.processor.apply_chat_template(
+            [self.messages, self.messages],
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            padding_side="left",
+        ).to(torch_device)
+
+        # it should not matter whether two images are the same size or not
+        output = model.generate(**inputs, max_new_tokens=30)
+        generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, output)]
+        result = self.processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+        EXPECTED_DECODED_TEXT = ["生甘草", "生甘草"]
+
+        self.assertEqual(
+            result,
+            EXPECTED_DECODED_TEXT,
+        )