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tests/models/perception_lm/test_modeling_perception_lm.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 PyTorch PerceptionLM model."""
+
+import unittest
+
+from huggingface_hub import hf_hub_download
+
+from transformers import (
+    AutoProcessor,
+    BitsAndBytesConfig,
+    PerceptionLMConfig,
+    PerceptionLMForConditionalGeneration,
+    PerceptionLMModel,
+    is_torch_available,
+)
+from transformers.testing_utils import (
+    cleanup,
+    require_bitsandbytes,
+    require_torch,
+    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
+
+
+if is_torch_available():
+    import torch
+
+
+class PerceptionLMVisionText2TextModelTester:
+    def __init__(
+        self,
+        parent,
+        image_token_id=0,
+        video_token_id=2,
+        seq_length=7,
+        tie_word_embeddings=True,
+        projector_pooling_ratio=1,
+        text_config={
+            "model_type": "llama",
+            "seq_length": 7,
+            "is_training": True,
+            "use_input_mask": True,
+            "use_token_type_ids": False,
+            "use_labels": True,
+            "vocab_size": 99,
+            "hidden_size": 32,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "intermediate_size": 37,
+            "hidden_act": "gelu",
+            "hidden_dropout_prob": 0.1,
+            "attention_probs_dropout_prob": 0.1,
+            "max_position_embeddings": 512,
+            "type_vocab_size": 16,
+            "type_sequence_label_size": 2,
+            "initializer_range": 0.02,
+            "num_labels": 3,
+            "num_choices": 4,
+            "pad_token_id": 1,
+        },
+        is_training=True,
+        vision_config={
+            "architecture": "vit_pe_core_large_patch14_336",
+            "model_args": {
+                "embed_dim": 64,
+                "img_size": (14, 14),
+                "depth": 2,
+                "global_pool": "",
+                "use_post_transformer_norm": False,
+                "init_values": 0.1,
+                "ref_feat_shape": (1, 1),
+            },
+        },
+    ):
+        self.parent = parent
+        self.image_token_id = image_token_id
+        self.video_token_id = video_token_id
+        self.text_config = text_config
+        self.vision_config = vision_config
+        self.pad_token_id = text_config["pad_token_id"]
+
+        self.num_hidden_layers = text_config["num_hidden_layers"]
+        self.vocab_size = text_config["vocab_size"]
+        self.hidden_size = text_config["hidden_size"]
+        self.num_attention_heads = text_config["num_attention_heads"]
+        self.is_training = is_training
+        self.tie_word_embeddings = tie_word_embeddings
+
+        self.batch_size = 3
+        self.num_tiles = 1
+        self.num_frames = 1
+        self.num_channels = 3
+        self.image_size = self.vision_config["model_args"]["img_size"][0]
+        self.num_image_tokens = (self.vision_config["model_args"]["img_size"][0] // 14) ** 2
+        self.num_video_tokens = (self.vision_config["model_args"]["img_size"][0] // 14) ** 2
+        self.seq_length = seq_length + self.num_image_tokens
+        self.encoder_seq_length = self.seq_length
+
+    def get_config(self):
+        return PerceptionLMConfig(
+            text_config=self.text_config,
+            vision_config=self.vision_config,
+            vision_use_cls_token=True,
+            image_token_id=self.image_token_id,
+            video_token_id=self.video_token_id,
+            tie_word_embeddings=self.tie_word_embeddings,
+        )
+
+    def prepare_config_and_inputs(self):
+        pixel_values = floats_tensor(
+            [
+                self.batch_size,
+                self.num_tiles,
+                self.num_channels,
+                self.vision_config["model_args"]["img_size"][0],
+                self.vision_config["model_args"]["img_size"][1],
+            ]
+        )
+        pixel_values_videos = floats_tensor(
+            [
+                self.batch_size,
+                self.num_frames,
+                self.num_channels,
+                self.vision_config["model_args"]["img_size"][0],
+                self.vision_config["model_args"]["img_size"][1],
+            ]
+        )
+        config = self.get_config()
+
+        return config, pixel_values, pixel_values_videos
+
+    def prepare_config_and_inputs_for_common(self):
+        config, pixel_values, pixel_values_videos = self.prepare_config_and_inputs()
+        input_ids = ids_tensor([self.batch_size, self.seq_length], config.text_config.vocab_size - 2) + 2
+        attention_mask = torch.ones(input_ids.shape, dtype=torch.long).to(torch_device)
+        input_ids[input_ids == config.image_token_id] = self.pad_token_id
+        input_ids[input_ids == config.video_token_id] = self.pad_token_id
+        input_ids[:, : self.num_image_tokens] = config.image_token_id
+        input_ids[:, self.num_image_tokens : self.num_video_tokens + self.num_image_tokens] = config.video_token_id
+
+        inputs_dict = {
+            "pixel_values": pixel_values,
+            "pixel_values_videos": pixel_values_videos,
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class PerceptionLMForConditionalGenerationModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase):
+    """
+    Model tester for `PerceptionLMForConditionalGeneration`.
+    """
+
+    all_model_classes = (
+        (
+            PerceptionLMModel,
+            PerceptionLMForConditionalGeneration,
+        )
+        if is_torch_available()
+        else ()
+    )
+
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = PerceptionLMVisionText2TextModelTester(self)
+        common_properties = [
+            "image_token_id",
+            "video_token_id",
+        ]
+        self.config_tester = ConfigTester(
+            self,
+            config_class=PerceptionLMConfig,
+            has_text_modality=False,
+            common_properties=common_properties,
+        )
+
+    def test_config(self):
+        self.config_tester.run_common_tests()
+
+    # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
+    def test_inputs_embeds(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)
+
+            input_ids = inputs["input_ids"]
+            del inputs["input_ids"]
+            del inputs["pixel_values"]
+            del inputs["pixel_values_videos"]
+
+            wte = model.get_input_embeddings()
+            inputs["inputs_embeds"] = wte(input_ids)
+
+            with torch.no_grad():
+                model(**inputs)
+
+    # overwrite inputs_embeds tests because we need to delete "pixel values" for LVLMs
+    # while some other models require pixel_values to be present
+    def test_inputs_embeds_matches_input_ids(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)
+            input_ids = inputs["input_ids"]
+            del inputs["input_ids"]
+            del inputs["pixel_values"]
+            del inputs["pixel_values_videos"]
+
+            inputs_embeds = model.get_input_embeddings()(input_ids)
+
+            with torch.no_grad():
+                out_ids = model(input_ids=input_ids, **inputs)[0]
+                out_embeds = model(inputs_embeds=inputs_embeds, **inputs)[0]
+            torch.testing.assert_close(out_embeds, out_ids)
+
+    def test_mismatching_num_image_tokens(self):
+        """
+        Tests that VLMs through an error with explicit message saying what is wrong
+        when number of images doesn't match number of image tokens in the text.
+        Also we need to test multi-image cases when one prompr has multiple image tokens.
+        """
+        config, input_dict = self.model_tester.prepare_config_and_inputs_for_common()
+        for model_class in self.all_model_classes:
+            if model_class == PerceptionLMModel:
+                continue
+            model = model_class(config).to(torch_device)
+            model.eval()
+            _ = model(**input_dict)  # successful forward with no modifications
+
+            # remove one image but leave the image token in text
+            input_dict["pixel_values"] = input_dict["pixel_values"][-1:, ...]
+            with self.assertRaisesRegex(ValueError, "Image features and image tokens do not match"):
+                _ = model(**input_dict)
+
+            # simulate multi-image case by concatenating inputs where each has exactly one image/image-token
+            input_ids = input_dict["input_ids"][:1]
+            pixel_values = input_dict["pixel_values"][: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)
+
+            # two images and two image tokens don't raise an error
+            pixel_values = torch.cat([pixel_values, pixel_values], dim=0)
+            _ = model(input_ids=input_ids, pixel_values=pixel_values)
+
+    def test_training(self):
+        self.all_model_classes = (PerceptionLMForConditionalGeneration,) if is_torch_available() else ()
+        super().test_training()
+
+    def test_training_gradient_checkpointing(self):
+        self.all_model_classes = (PerceptionLMForConditionalGeneration,) if is_torch_available() else ()
+        super().test_training_gradient_checkpointing()
+
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        self.all_model_classes = (PerceptionLMForConditionalGeneration,) if is_torch_available() else ()
+        super().test_training_gradient_checkpointing_use_reentrant_false()
+
+    def test_training_gradient_checkpointing_use_reentrant_true(self):
+        self.all_model_classes = (PerceptionLMForConditionalGeneration,) if is_torch_available() else ()
+        super().test_training_gradient_checkpointing_use_reentrant_true()
+
+    @unittest.skip(reason="The model has TimmWrapper backbone but doesn't apply any conversion")
+    def test_reverse_loading_mapping(self, check_keys_were_modified=True):
+        pass
+
+    @unittest.skip(
+        reason="PE/TIMM's attention implementation is self configured and won't raise ValueError on global attention implementation."
+    )
+    def test_flash_attn_2_can_dispatch_composite_models(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_flash_attention_2_padding_matches_padding_free_with_position_ids(self):
+        pass
+
+    @unittest.skip("ViT PE / TimmWrapperModel cannot be tested with meta device")
+    def test_can_be_initialized_on_meta(self):
+        pass
+
+    @unittest.skip("Specifying both inputs_embeds and pixel_values are not supported for PerceptionLM")
+    def test_generate_from_inputs_embeds_0_greedy(self):
+        pass
+
+    @unittest.skip("Specifying both inputs_embeds and pixel_values are not supported for PerceptionLM")
+    def test_generate_from_inputs_embeds_1_beam_search(self):
+        pass
+
+    @unittest.skip("Specifying both inputs_embeds and pixel_values are not supported for PerceptionLM")
+    def test_generate_from_inputs_embeds_with_static_cache(self):
+        pass
+
+    ## Skip flash attention releated tests below
+    ## correct configuration:
+    ## from_pretrained(model_id, attn_implementation={"text_config": "flash_attention_2", "vision_config": "eager"}
+    @unittest.skip("Flash attn test is not configured correctly as we need to configure vision/timm model to 'eager'.")
+    def test_eager_matches_fa2_generate(self):
+        pass
+
+    @unittest.skip("Flash attn test is not configured correctly as we need to configure vision/timm model to 'eager'.")
+    def test_flash_attn_2_fp32_ln(self):
+        pass
+
+    @unittest.skip("Flash attn test is not configured correctly as we need to configure vision/timm model to 'eager'.")
+    def test_flash_attn_2_from_config(self):
+        pass
+
+    @unittest.skip("SDPA test is not configured correctly as we need to configure vision/timm model to 'eager'.")
+    def test_eager_matches_sdpa_generate_with_dynamic_cache(self):
+        pass
+
+    @unittest.skip("Flash attn test is not configured correctly as we need to configure vision/timm model to 'eager'.")
+    def test_flash_attn_2_inference_equivalence_right_padding(self):
+        pass
+
+    @unittest.skip("SDPA test is not configured correctly as we need to configure vision/timm model to 'eager'.")
+    def test_eager_matches_sdpa_generate(self):
+        pass
+
+    @unittest.skip("Flash attn test is not configured correctly as we need to configure vision/timm model to 'eager'.")
+    def test_flash_attn_2_inference_equivalence(self):
+        pass
+
+    @unittest.skip(
+        "PerceptionLMForConditionalGeneration does not have language_model, vision_tower, multi_modal_projector."
+    )
+    def test_sdpa_can_dispatch_composite_models(self):
+        pass
+
+    @unittest.skip("Cannot set `output_attentions` for timm models.")
+    def test_attention_outputs(self):
+        pass
+
+    @unittest.skip("Cannot set `output_attentions` for timm models.")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip("Cannot set `output_attentions` for timm models.")
+    def test_generate_compilation_all_outputs(self):
+        pass
+
+    @unittest.skip("Cannot set output_attentions on timm models.")
+    def test_get_image_features_attentions(self):
+        pass
+
+    def _image_features_get_expected_num_hidden_states(self, model_tester=None):
+        # For models that rely on timm for their vision backend, it's hard to infer how many layers the model has
+        # from the timm config alone. So, we're just hardcoding the expected number of hidden states here.
+        return 2
+
+
+TEST_MODEL_PATH = "facebook/Perception-LM-1B"
+
+
+@require_torch
+@require_bitsandbytes
+@slow
+class PerceptionLMForConditionalGenerationIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.processor = AutoProcessor.from_pretrained(TEST_MODEL_PATH)
+        self.image_file = hf_hub_download(
+            repo_id="shumingh/perception_lm_test_images",
+            filename="14496_0.PNG",
+            repo_type="dataset",
+        )
+        self.video_file = hf_hub_download(
+            repo_id="shumingh/perception_lm_test_videos",
+            filename="GUWR5TyiY-M_000012_000022.mp4",
+            repo_type="dataset",
+        )
+        self.conversation1 = [
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "url": self.image_file},
+                    {"type": "text", "text": "Describe the bar plot in the image."},
+                ],
+            }
+        ]
+        self.conversation2 = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "video",
+                        "url": self.video_file,
+                    },
+                    {"type": "text", "text": "Can you describe the video in detail?"},
+                ],
+            }
+        ]
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    def test_small_model_integration_test(self):
+        model = PerceptionLMForConditionalGeneration.from_pretrained(
+            TEST_MODEL_PATH, quantization_config=BitsAndBytesConfig(load_in_4bit=True), cache_dir="./"
+        )
+
+        inputs = self.processor.apply_chat_template(
+            [self.conversation1],
+            num_frames=32,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            padding_side="left",
+        ).to(torch_device)
+
+        generate_ids = model.generate(**inputs, max_new_tokens=18)
+        input_length = inputs["input_ids"].shape[1]
+        generate_ids_without_inputs = generate_ids[:, input_length:]
+
+        EXPECTED_DECODED_TEXT = "The bar plot displays the values of four categories: step, horror, mood, and lumber"  # fmt: skip
+
+        self.assertEqual(
+            self.processor.decode(generate_ids_without_inputs[0], skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    def test_small_model_integration_test_batched(self):
+        model = PerceptionLMForConditionalGeneration.from_pretrained(
+            TEST_MODEL_PATH, quantization_config=BitsAndBytesConfig(load_in_4bit=True)
+        )
+        processor = AutoProcessor.from_pretrained(TEST_MODEL_PATH)
+        inputs = processor.apply_chat_template(
+            [self.conversation1, self.conversation2],
+            num_frames=32,
+            add_generation_prompt=True,
+            tokenize=True,
+            return_dict=True,
+            return_tensors="pt",
+            padding=True,
+            padding_side="left",
+        ).to(torch_device)
+
+        generate_ids = model.generate(**inputs, max_new_tokens=18)
+        input_length = inputs["input_ids"].shape[1]
+        generate_ids_without_inputs = generate_ids[:, input_length:]
+
+        EXPECTED_DECODED_TEXT = ['The bar plot displays the values of four categories: step, horror, mood, and lumber', 'The video shows a group of people in green shirts and white shorts performing a jump rope routine']  # fmt: skip
+
+        self.assertEqual(
+            processor.batch_decode(generate_ids_without_inputs, skip_special_tokens=True),
+            EXPECTED_DECODED_TEXT,
+        )
+
+    def test_generation_no_images(self):
+        # model_id = "facebook/Perception-LM-1B"
+        model = PerceptionLMForConditionalGeneration.from_pretrained(
+            TEST_MODEL_PATH, quantization_config=BitsAndBytesConfig(load_in_4bit=True)
+        )
+        processor = AutoProcessor.from_pretrained(TEST_MODEL_PATH)
+
+        # Prepare inputs with no images
+        inputs = processor(text="Hello, I am", return_tensors="pt").to(torch_device)
+
+        # Make sure that `generate` works
+        _ = model.generate(**inputs, max_new_tokens=20)