first commit

tests/models/pe_audio_video/test_modeling_pe_audio_video.py

@@ -0,0 +1,304 @@
+# 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 huggingface_hub import hf_hub_download
+
+from transformers import PeAudioVideoEncoderConfig, PeAudioVideoProcessor
+from transformers.testing_utils import (
+    cleanup,
+    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,
+)
+
+
+if is_torch_available():
+    import torch
+
+    from transformers import (
+        PeAudioVideoEncoder,
+        PeAudioVideoModel,
+    )
+
+
+class PeAudioVideoEncoderTester:
+    def __init__(
+        self,
+        parent,
+        config_kwargs={
+            "audio_config": {
+                "dac_config": {
+                    "encoder_hidden_size": 16,
+                    "downsampling_ratios": [2, 4, 4],
+                    "decoder_hidden_size": 16,
+                    "n_codebooks": 6,
+                    "codebook_size": 512,
+                    "codebook_dim": 32,
+                    "quantizer_dropout": 0.0,
+                    "commitment_loss_weight": 0.25,
+                    "codebook_loss_weight": 1.0,
+                },
+                "hidden_size": 32,
+                "intermediate_size": 37,
+                "num_hidden_layers": 2,
+                "num_attention_heads": 2,
+                "num_key_value_heads": 2,
+                "head_dim": 128,
+                "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,
+            },
+            "video_config": {
+                "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": 128,
+                "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,
+            },
+            "hidden_size": 32,
+            "intermediate_size": 37,
+            "num_hidden_layers": 2,
+            "num_attention_heads": 2,
+            "num_key_value_heads": 2,
+            "head_dim": 128,
+            "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=12,
+        num_audio_channels=1,
+        num_video_channels=3,
+        audio_seq_length=160,
+        num_frames=24,
+        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_audio_channels = num_audio_channels
+        self.num_video_channels = num_video_channels
+        self.audio_seq_length = audio_seq_length
+        self.num_frames = num_frames
+        self.is_training = is_training
+
+    @property
+    def seq_length(self):
+        config = self.get_config()
+        # seq_length is what gets feeded to the transformer
+        # we first have to divide by hop_length to get the number of frames
+        # then we add 1 because we add the class token
+        return self.audio_seq_length // config.audio_config.dac_config.hop_length + 1
+
+    def prepare_config_and_inputs(self):
+        input_values = floats_tensor([self.batch_size, self.num_audio_channels, self.audio_seq_length])
+        # Generate valid_lengths in range [1, self.audio_seq_length] to ensure at least one valid frame
+        valid_audio_lengths = ids_tensor([self.batch_size], self.audio_seq_length - 1) + 1
+        padding_mask = torch.arange(self.audio_seq_length, device=torch_device)[None, :] < valid_audio_lengths[:, None]
+        padding_mask = padding_mask.int()
+
+        pixel_values_videos = floats_tensor(
+            [
+                self.batch_size,
+                self.num_frames,
+                self.num_video_channels,
+                self.config_kwargs["video_config"]["vision_config"]["model_args"]["img_size"][0],
+                self.config_kwargs["video_config"]["vision_config"]["model_args"]["img_size"][1],
+            ]
+        )
+        # Generate valid_lengths in range [1, self.num_frames] to ensure at least one valid frame
+        valid_video_lengths = ids_tensor([self.batch_size], self.num_frames - 1) + 1
+        padding_mask_videos = (
+            torch.arange(self.num_frames, device=torch_device)[None, :] < valid_video_lengths[:, None]
+        )
+        padding_mask_videos = padding_mask_videos.int()
+
+        config = self.get_config()
+
+        return config, input_values, padding_mask, pixel_values_videos, padding_mask_videos
+
+    def get_config(self):
+        if not hasattr(self, "_config"):
+            self._config = PeAudioVideoEncoderConfig(**self.config_kwargs)
+        return self._config
+
+    def create_and_check_model(self, config, input_values, padding_mask, pixel_values_videos, padding_mask_videos):
+        model = PeAudioVideoEncoder(config=config)
+        model.to(torch_device)
+        model.eval()
+        with torch.no_grad():
+            result = model(
+                input_values,
+                padding_mask=padding_mask,
+                pixel_values_videos=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, input_values, padding_mask, pixel_values_videos, padding_mask_videos = config_and_inputs
+        inputs_dict = {
+            "input_values": input_values,
+            "padding_mask": padding_mask,
+            "pixel_values_videos": pixel_values_videos,
+            "padding_mask_videos": padding_mask_videos,
+        }
+        return config, inputs_dict
+
+
+@require_torch
+class PeAudioVideoEncoderTest(ModelTesterMixin, unittest.TestCase):
+    all_model_classes = (PeAudioVideoEncoder,)
+    additional_model_inputs = ["pixel_values_videos", "padding_mask_videos"]
+    test_resize_embeddings = False
+    _is_composite = True
+    test_torch_exportable = False
+
+    def setUp(self):
+        self.model_tester = PeAudioVideoEncoderTester(self)
+        self.config_tester = ConfigTester(
+            self, config_class=PeAudioVideoEncoderConfig, 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="PeAudioVideoEncoder does not have usual input embeddings")
+    def test_model_get_set_embeddings(self):
+        pass
+
+    @unittest.skip("PeAudioVideoEncoder does not have language_model, vision_tower, multi_modal_projector.")
+    def test_sdpa_can_dispatch_composite_models(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("ViT PE / TimmWrapperModel cannot be tested with meta device")
+    def test_can_be_initialized_on_meta(self):
+        pass
+
+    @unittest.skip("ViT PE / TimmWrapperModel cannot be tested with meta device")
+    def test_can_load_with_meta_device_context_manager(self):
+        pass
+
+    @unittest.skip("PeAudioVideoEncoder does not support feed forward chunking")
+    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 just the class embedding!")
+    def test_can_init_all_missing_weights(self):
+        pass
+
+
+@require_torch
+class PeAudioVideoModelIntegrationTest(unittest.TestCase):
+    def setUp(self):
+        self.checkpoint_name = "/raid/eustache/sam-audio/converted"
+        self.dtype = torch.float32
+        self.processor = PeAudioVideoProcessor.from_pretrained("facebook/pe-av-large")
+
+    def tearDown(self):
+        cleanup(torch_device, gc_collect=True)
+
+    @slow
+    @unittest.skip(reason="TODO when released")
+    def test(self):
+        video_path = hf_hub_download(
+            repo_id="eustlb/dummy-video-dataset", filename="audiobox.mp4", repo_type="dataset"
+        )
+        audio_path = hf_hub_download(
+            repo_id="eustlb/dummy-video-dataset", filename="audiobox.mp4", repo_type="dataset"
+        )
+
+        inputs = self.processor(
+            text=["A woman and a man speaking", "A woman speaking"],
+            audio=[audio_path, "/home/eustache_lebihan/add-sam-audio/audiobox_first5sec.mp4"],
+            videos=[video_path, "/home/eustache_lebihan/add-sam-audio/audiobox_first5sec.mp4"],
+            return_tensors="pt",
+            padding=True,
+        ).to(torch_device)
+        model = PeAudioVideoModel.from_pretrained(
+            self.checkpoint_name, dtype=self.dtype, device_map=torch_device, attn_implementation="eager"
+        )
+
+        with torch.no_grad():
+            outputs = model(**inputs)
+            print(outputs)