Add V-JEPA 2

docs/source/en/model_doc/vjepa2.md

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+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+    </div>
+</div>
+
+# V-JEPA 2
+
+V-JEPA 2 is a self-supervised approach to training video encoders developed by FAIR, Meta. Using internet-scale video data, VJEPA 2 attains state-of-the-art performance on motion understanding and human action anticpation tasks. V-JEPA 2-AC is a latent action-conditioned world model post-trained from V-JEPA 2 (using a small amount of robot trajectory interaction data) that solves robot manipulation tasks without environment-specific data collection or task-specific training or calibration.
+
+You can find all original VJEPA2 checkpoints under the [VJEPA 2](https://huggingface.co/collections/facebook/v-jepa-2-6841bad8413014e185b497a6) collection.
+
+
+This model was contributed by [koustuvs](https://huggingface.co/koustuvs) and [yonigozlan](https://huggingface.co/yonigozlan). The original code can be found [here](https://github.com/facebookresearch/vjepa2).
+
+## Usage example
+
+The snippet below shows how to load the VJEPA 2 model using `AutoModel` class.
+
+```py
+import torch
+from torchcodec.decoders import VideoDecoder
+import numpy as np
+
+processor = AutoVideoProcessor.from_pretrained("facebook/vjepa2-vitl-fpc64-256")
+model = AutoModel.from_pretrained(
+    "facebook/vjepa2-vitl-fpc64-256",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+video_url = "https://huggingface.co/datasets/nateraw/kinetics-mini/resolve/main/val/archery/-Qz25rXdMjE_000014_000024.mp4"
+
+vr = VideoDecoder(video_url)
+frame_idx = np.arange(0, 64) # choosing some frames. here, you can define more complex sampling strategy
+video = vr.get_frames_at(indices=frame_idx).data  # T x C x H x W
+video = processor(video, return_tensors="pt").to(model.device)
+outputs = model(**video)
+
+# VJEPA-2 encoder outputs, same as calling `model.get_vision_features()`
+encoder_outputs = outputs.last_hidden_state
+
+# VJEPA-2 predictor outputs
+predictor_outputs = outputs.predictor_output.last_hidden_state
+```
+
+## VJEPA2Config
+
+[[autodoc]] VJEPA2Config
+
+## VJEPA2Model
+
+[[autodoc]] VJEPA2Model
+    - forward
+
+## VJEPA2VideoProcessor
+
+[[autodoc]] VJEPA2VideoProcessor

src/transformers/models/__init__.py

@@ -317,6 +317,7 @@
     from .vitpose_backbone import *
     from .vits import *
     from .vivit import *
+    from .vjepa2 import *
     from .wav2vec2 import *
     from .wav2vec2_bert import *
     from .wav2vec2_conformer import *

src/transformers/models/auto/configuration_auto.py

@@ -364,6 +364,7 @@
         ("vitpose_backbone", "VitPoseBackboneConfig"),
         ("vits", "VitsConfig"),
         ("vivit", "VivitConfig"),
+        ("vjepa2", "VJEPA2Config"),
         ("wav2vec2", "Wav2Vec2Config"),
         ("wav2vec2-bert", "Wav2Vec2BertConfig"),
         ("wav2vec2-conformer", "Wav2Vec2ConformerConfig"),
@@ -748,6 +749,7 @@
         ("vitpose_backbone", "ViTPoseBackbone"),
         ("vits", "VITS"),
         ("vivit", "ViViT"),
+        ("vjepa2", "VJEPA2Model"),
         ("wav2vec2", "Wav2Vec2"),
         ("wav2vec2-bert", "Wav2Vec2-BERT"),
         ("wav2vec2-conformer", "Wav2Vec2-Conformer"),

src/transformers/models/auto/modeling_auto.py

@@ -335,6 +335,7 @@
         ("vitdet", "VitDetModel"),
         ("vits", "VitsModel"),
         ("vivit", "VivitModel"),
+        ("vjepa2", "VJEPA2Model"),
         ("wav2vec2", "Wav2Vec2Model"),
         ("wav2vec2-bert", "Wav2Vec2BertModel"),
         ("wav2vec2-conformer", "Wav2Vec2ConformerModel"),

src/transformers/models/auto/video_processing_auto.py

@@ -56,6 +56,7 @@
             ("qwen2_vl", "Qwen2VLVideoProcessor"),
             ("smolvlm", "SmolVLMVideoProcessor"),
             ("video_llava", "VideoLlavaVideoProcessor"),
+            ("vjepa2", "VJEPA2VideoProcessor"),
         ]
     )
 

src/transformers/models/vjepa2/__init__.py

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+from typing import TYPE_CHECKING
+
+from ...utils import _LazyModule
+from ...utils.import_utils import define_import_structure
+
+
+if TYPE_CHECKING:
+    from .configuration_vjepa2 import *
+    from .modeling_vjepa2 import *
+else:
+    import sys
+
+    _file = globals()["__file__"]
+    sys.modules[__name__] = _LazyModule(__name__, _file, define_import_structure(_file), module_spec=__spec__)

src/transformers/models/vjepa2/configuration_vjepa2.py

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+"""VJEPA 2 model configuration"""
+
+from ...configuration_utils import PretrainedConfig
+
+
+class VJEPA2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`VJEPA2Model`]. It is used to instantiate an
+    VJEPA2 model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the VJEPA2
+    [facebook/vjepa2-vitl-fpc64-256](https://huggingface.co/facebook/vjepa2-vitl-fpc64-256) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        patch_size (`int`, *optional*, defaults to 16):
+            The size (resolution) of each patch.
+        crop_size (`int`, *optional*, defaults to 256):
+            Input resolution of the model
+        frames_per_clip (`int`, *optional*, defaults to 64):
+            The number of frames the model has been pretrained with. Does not impact inference.
+        tubelet_size (`int`, *optional*, defaults to 2):
+            The number of temporal frames used for a single rastor, check paper for more information.
+        hidden_size (`int`, *optional*, defaults to 1024):
+            Dimensionality of the encoder layers
+        in_chans (`int`, *optional*, defaults to 3):
+            The number of input channels
+        num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Encoder
+        num_hidden_layers (`int`, *optional*, defaults to 12):
+            The number of hidden layers
+        drop_path_rate (`float`, *optional*, defaults to 0.0):
+            Stochastic depth rate per sample (when applied in the main path of residual layers).
+        mlp_ratio (`float`, *optional*, defaults to 4.0):
+            Ratio of the hidden size of the MLPs used in Encoder relative to the `hidden_size`.
+        layer_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the layer normalization layers.
+        qkv_bias (`bool`, *optional*, defaults to `True`):
+            Whether to add a bias to the queries, keys and values.
+        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
+            The dropout probability for attentions.
+            The dropout probability for all fully connected layers.
+        hidden_act (`str`, *optional*, defaults to `"gelu"`):
+            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
+            `"relu"`, `"selu"` and `"gelu_new"` are supported.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        pred_hidden_size (`int`, *optional*, defaults to 384):
+            Dimensionality of the predictor layers
+        pred_num_attention_heads (`int`, *optional*, defaults to 12):
+            Number of attention heads for each attention layer in the Predictor
+        pred_num_hidden_layers (`int`, *optional*, defaults to 12):
+            Number of hidden layers in the Predictor
+        pred_num_mask_tokens (`int`, *optional*, defaults to 10):
+            Define the number of mask tokens to use in the Predictor
+        pred_zero_init_mask_tokens (`bool`, *optional*, defaults to `True`):
+            Initialize the mask tokens in the predictor with 0.
+        pred_mlp_ratio (`float`, *optional*, defaults to 4.0):
+            Ratio of the hidden size of the MLPs used in Predictor relative to the `pred_hidden_size`.
+
+    Example:
+
+    ```python
+    >>> from transformers import VJEPA2Config, VJEPA2Model
+
+    >>> # Initializing a VJEPA2 vjepa2-vitl-fpc64-256 style configuration
+    >>> configuration = VJEPA2Config()
+
+    >>> # Initializing a model (with random weights) from the vjepa2-vitl-fpc64-256  style configuration
+    >>> model = Dinov2Model(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "vjepa2"
+
+    def __init__(
+        self,
+        patch_size=16,
+        crop_size=256,
+        frames_per_clip=64,
+        tubelet_size=2,
+        hidden_size=1024,
+        in_chans=3,
+        num_attention_heads=12,
+        num_hidden_layers=12,
+        drop_path_rate=0.0,
+        mlp_ratio=4.0,
+        layer_norm_eps=1e-6,
+        qkv_bias=True,
+        attention_probs_dropout_prob=0.0,
+        hidden_act="gelu",
+        initializer_range=0.02,
+        # predictor params
+        pred_hidden_size=384,
+        pred_num_attention_heads=12,
+        pred_num_hidden_layers=12,
+        pred_num_mask_tokens=10,
+        pred_zero_init_mask_tokens=True,
+        pred_mlp_ratio=4.0,
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+
+        self.crop_size = crop_size
+        self.frames_per_clip = frames_per_clip
+        self.patch_size = patch_size
+        self.tubelet_size = tubelet_size
+        self.hidden_size = hidden_size
+        self.in_chans = in_chans
+        self.num_attention_heads = num_attention_heads
+        self.num_hidden_layers = num_hidden_layers
+        self.drop_path_rate = drop_path_rate
+        self.mlp_ratio = mlp_ratio
+        self.layer_norm_eps = layer_norm_eps
+        self.qkv_bias = qkv_bias
+        self.attention_probs_dropout_prob = attention_probs_dropout_prob
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.image_size = crop_size
+        # predictor params
+        self.pred_hidden_size = pred_hidden_size
+        self.pred_num_attention_heads = pred_num_attention_heads
+        self.pred_num_hidden_layers = pred_num_hidden_layers
+        self.pred_num_mask_tokens = pred_num_mask_tokens
+        self.pred_zero_init_mask_tokens = pred_zero_init_mask_tokens
+        self.pred_mlp_ratio = pred_mlp_ratio
+
+
+__all__ = ["VJEPA2Config"]