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tests/models/pe_video/test_modeling_pe_video.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.
+import unittest
+
+from transformers import PeVideoConfig, PeVideoEncoderConfig
+from transformers.testing_utils import (
+    require_torch,
+    slow,
+    torch_device,
+)
+from transformers.utils import is_torch_available
+
+from ...test_configuration_common import ConfigTester
+from ...test_modeling_common import (
+    ModelTesterMixin,
+    floats_tensor,
+    ids_tensor,
+    random_attention_mask,
+    require_torch_gpu,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        ModernBertConfig,
+        PeVideoEncoder,
+        PeVideoModel,
+    )
+
+
+class PeVideoEncoderTester:
+    def __init__(
+        self,
+        parent,
+        config_kwargs={
+            "vision_config": {
+                "architecture": "vit_pe_core_large_patch14_336",
+                "model_args": {
+                    "embed_dim": 64,
+                    "img_size": (14, 14),
+                    "depth": 2,
+                },
+                "num_classes": 4,
+            },
+            "hidden_size": 32,
+            "intermediate_size": 37,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 2,
+            "num_key_value_heads": 2,
+            "head_dim": 16,
+            "hidden_act": "silu",
+            "max_position_embeddings": 512,
+            "initializer_range": 0.02,
+            "rms_norm_eps": 1e-5,
+            "use_cache": True,
+            "rope_theta": 20000,
+            "rope_scaling": None,
+            "attention_bias": False,
+            "max_window_layers": 28,
+            "attention_dropout": 0.0,
+        },
+        batch_size=4,
+        num_frames=8,
+        num_channels=3,
+        is_training=True,
+    ):
+        self.parent = parent
+
+        self.config_kwargs = config_kwargs
+        for key, value in config_kwargs.items():
+            setattr(self, key, value)
+
+        self.batch_size = batch_size
+        self.num_frames = num_frames
+        self.num_channels = num_channels
+        self.is_training = is_training
+
+    @property
+    def seq_length(self):
+        # seq_length is what gets fed to the transformer
+        # we add 1 because we add the class token
+        return self.num_frames + 1
+
+    def prepare_config_and_inputs(self):
+        pixel_values_videos = floats_tensor(
+            [
+                self.batch_size,
+                self.num_frames,
+                self.num_channels,
+                self.config_kwargs["vision_config"]["model_args"]["img_size"][0],
+                self.config_kwargs["vision_config"]["model_args"]["img_size"][1],
+            ]
+        )
+        # Generate valid_lengths in range [1, num_frames] to ensure at least one valid frame
+        valid_lengths = ids_tensor([self.batch_size], self.num_frames - 1) + 1
+        padding_mask_videos = torch.arange(self.num_frames, device=torch_device).unsqueeze(0) < valid_lengths[:, None]
+        padding_mask_videos = padding_mask_videos.int()
+        config = self.get_config()
+
+        return config, pixel_values_videos, padding_mask_videos
+
+    def get_config(self):
+        return PeVideoEncoderConfig(**self.config_kwargs)
+
+    def create_and_check_model(self, config, pixel_values_videos, padding_mask_videos):
+        model = PeVideoEncoder(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(pixel_values_videos, padding_mask_videos=padding_mask_videos)
+        self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values_videos, padding_mask_videos = config_and_inputs
+        inputs_dict = {"pixel_values_videos": pixel_values_videos, "padding_mask_videos": padding_mask_videos}
+        return config, inputs_dict
+
+
+@require_torch
+class PeVideoEncoderTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (PeVideoEncoder,)
+    test_resize_embeddings = False
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = PeVideoEncoderTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=PeVideoEncoderConfig, has_text_modality=False, hidden_size=37
+        )
+
+    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)
+
+    @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="Timm Eva (PE) weights cannot be fully constructed in _init_weights")
+    def test_can_init_all_missing_weights(self):
+        pass
+
+    @unittest.skip(reason="PeVideoEncoder does not have usual input embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip("Cannot set `output_attentions` for timm models.")
+    def test_attention_outputs(self):
+        pass
+
+    @unittest.skip("TimmWrapperModel cannot be tested with meta device")
+    def test_can_be_initialized_on_meta(self):
+        pass
+
+    @unittest.skip("TimmWrapperModel cannot be tested with meta device")
+    def test_can_load_with_meta_device_context_manager(self):
+        pass
+
+    @unittest.skip("Cannot set `output_attentions` for timm models.")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="PeVideoEncoder does not support feedforward chunking yet")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @unittest.skip(reason="PeAudioModel uses some timm stuff not compatible")
+    def test_save_load(self):
+        pass
+
+    @unittest.skip(reason="@eustlb this is not really expected")
+    def test_batching_equivalence(self):
+        pass
+
+
+class PeVideoTextModelTester:
+    """
+    Only a ModelTester and no PeVideoTextModelTest since text model is ModernBertModel that is already tested.
+    """
+
+    def __init__(
+        self,
+        parent,
+        config_kwargs={
+            "vocab_size": 99,
+            "pad_token_id": 0,
+            "hidden_size": 32,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 4,
+            "intermediate_size": 37,
+            "hidden_activation": "gelu",
+            "mlp_dropout": 0.0,
+            "attention_dropout": 0.0,
+            "embedding_dropout": 0.0,
+            "classifier_dropout": 0.0,
+            "max_position_embeddings": 512,
+            "type_vocab_size": 16,
+            "is_decoder": False,
+            "initializer_range": 0.02,
+        },
+        batch_size=4,
+        seq_length=7,
+        is_training=True,
+        use_input_mask=True,
+        use_labels=True,
+    ):
+        self.parent = parent
+
+        self.config_kwargs = config_kwargs
+        for key, value in config_kwargs.items():
+            setattr(self, key, value)
+
+        self.batch_size = batch_size
+        self.seq_length = seq_length
+        self.is_training = is_training
+        self.use_input_mask = use_input_mask
+        self.use_labels = use_labels
+
+    def prepare_config_and_inputs(self):
+        input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
+
+        input_mask = None
+        if self.use_input_mask:
+            input_mask = random_attention_mask([self.batch_size, self.seq_length])
+
+        config = self.get_config()
+
+        return config, input_ids, input_mask
+
+    def get_config(self):
+        return ModernBertConfig(**self.config_kwargs)
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, input_mask = config_and_inputs
+        inputs_dict = {"input_ids": input_ids, "attention_mask": input_mask}
+        return config, inputs_dict
+
+
+class PeVideoModelTester:
+    def __init__(self, parent, text_kwargs=None, video_kwargs=None, is_training=True):
+        if text_kwargs is None:
+            text_kwargs = {}
+        if video_kwargs is None:
+            video_kwargs = {}
+
+        self.parent = parent
+        self.text_model_tester = PeVideoTextModelTester(parent, **text_kwargs)
+        self.video_model_tester = PeVideoEncoderTester(parent, **video_kwargs)
+        self.batch_size = self.text_model_tester.batch_size  # need bs for batching_equivalence test
+        self.is_training = is_training
+
+    def prepare_config_and_inputs(self):
+        _, input_ids, attention_mask = self.text_model_tester.prepare_config_and_inputs()
+        _, pixel_values_videos, padding_mask_videos = self.video_model_tester.prepare_config_and_inputs()
+
+        config = self.get_config()
+
+        return config, input_ids, attention_mask, pixel_values_videos, padding_mask_videos
+
+    def get_config(self):
+        text_config = self.text_model_tester.get_config()
+        video_config = self.video_model_tester.get_config()
+        return PeVideoConfig(
+            text_config=text_config.to_dict(),
+            video_config=video_config.to_dict(),
+            projection_dim=32,
+        )
+
+    def create_and_check_model(self, config, input_ids, attention_mask, pixel_values_videos, padding_mask_videos):
+        model = PeVideoModel(config).to(torch_device).eval()
+        with torch.no_grad():
+            _ = model(input_ids, pixel_values_videos, attention_mask, padding_mask_videos)
+
+        # TODO: there is no logits per video for now
+        # self.parent.assertEqual(result.logits_per_video.shape, (self.video_model_tester.batch_size, self.text_model_tester.batch_size))
+        # self.parent.assertEqual(result.logits_per_text.shape, (self.text_model_tester.batch_size, self.video_model_tester.batch_size))
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, input_ids, attention_mask, pixel_values_videos, padding_mask_videos = config_and_inputs
+        inputs_dict = {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "pixel_values_videos": pixel_values_videos,
+            "padding_mask_videos": padding_mask_videos,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class PeVideoModelTest(ModelTesterMixin, unittest.TestCase):
+    # TODO: add PipelineTesterMixin
+    all_model_classes = (PeVideoModel,)
+    additional_model_inputs = ["pixel_values_videos", "padding_mask_videos"]
+    test_resize_embeddings = False
+    has_attentions = False
+    _is_composite = True
+
+    def setUp(self):
+        self.model_tester = PeVideoModelTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=PeVideoConfig, has_text_modality=False, common_properties=[], hidden_size=37
+        )
+
+    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)
+
+    @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="PeVideoModel does not have usual input embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip(
+        "TimmWrapperForImageClassification does not support an attention implementation through torch.nn.functional.scaled_dot_product_attention yet."
+    )
+    def test_can_set_attention_dynamically_composite_model(self):
+        pass
+
+    @unittest.skip(reason="Hidden_states is tested in individual model tests")
+    def test_hidden_states_output(self):
+        pass
+
+    @unittest.skip(reason="Retain_grad is tested in individual model tests")
+    def test_retain_grad_hidden_states_attentions(self):
+        pass
+
+    @unittest.skip(reason="PeVideoModel does not support feed forward chunking yet")
+    def test_feed_forward_chunking(self):
+        pass
+
+    @unittest.skip("#TODO @eustlb this should be fixed tho")
+    def test_save_load(self):
+        pass
+
+    @unittest.skip(reason="@eustlb this is not really expected")
+    def test_batching_equivalence(self):
+        pass
+
+    @unittest.skip(reason="@eustlb this is not really expected")
+    def test_can_init_all_missing_weights(self):
+        pass
+
+    @require_torch_gpu  # pe-video contains triton code which cannot run on CPU, so we only test on GPU
+    def test_all_tensors_are_parameter_or_buffer(self):
+        super().test_all_tensors_are_parameter_or_buffer()
+
+
+@require_torch
+class PeVideoIntegrationTest(unittest.TestCase):
+    @slow
+    def test_inference(self):
+        # TODO: Add integration test when pretrained model is available
+        pass