tl;dr Our work introduces the Block-Recurrent Hypothesis (BRH), by noticing that foundation models like DINOv2 can be rewritten using only two recurrent blocks to recover 96% of the original accuracy. We leverage our framework and explore a Dynamical Interpretability approach where we interpret token evolution through layers as trajectories and show that they converge into class-dependent angular basins while late-stage updates collapse into low-rank attractors.
Ultimately, the study reveals that Vision Transformers seems to naturally converge toward compact, iterative programs instead of unique layer-by-layer transformations (indicating a lower algorithmic complexity / Kolmogorov complexity).
Setup
Environment
To run the code, you will need to create a mamba (or conda) environment from the environment.yml file.
Create and activate the environment with
mamba env create -f environment.yml
mamba activate raptor
Paths
Edit src/paths.py to have the correct absolute paths to different datasets.
Extracting DINOv2 Activations for ImageNet-1k
For ImageNet, we precompute the DINOv2 activations so that Raptor can train faster.
We provide a script to extract the activations from the ImageNet-1k dataset. This script is available in the data directory.
This script takes around 5 hours to run on 1 H100 GPU, and storing the activations requires a lot of disk space.
cd data
python precompute_dinov2_act.py
Download Pretrained Classifiers
Download the DINOv2 linear heads from Meta’s repository.
These are used during training of Raptor.
cd src
wget https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_reg4_linear_head.pth
wget https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_reg4_linear_head.pth
cp dinov2_vitb14_reg4_linear_head.pth imagenet_probes/dinov2_vitb14_reg4_linear_head.pth
cp dinov2_vits14_reg4_linear_head.pth imagenet_probes/dinov2_vits14_reg4_linear_head.pth
Usage Example
Raptor training follows 4 main steps. Here, we show example usage for a 3-block Raptor:
- Determine max-cut segmentations. This has been done for you in src/000_max_cut_dinov2_base.ipynb.
- Train each block independently.
cd src python trainer.py --teacher_force --mse --sigma 0 --lr 3e-4 --wandb --t_scale --swiglu --start_layer 0 --end_layer 7 --seed 100 python trainer.py --teacher_force --mse --sigma 0 --lr 3e-4 --wandb --t_scale --swiglu --start_layer 7 --end_layer 10 --seed 101 python trainer.py --teacher_force --weighted --sigma 0 --lr 3e-4 --wandb --t_scale --swiglu --start_layer 10 --end_layer 12 --seed 104 - Train the full model with the pretrained blocks.
cd src BP1="final_weighted_False_autoregressive_False_distillation_False_teacher_True_mse_True_cosine_False_t_scale_True_swiglu_True_sigma_0.0_start_0_end_7_lr_0.0003_cls_weight_0.34_reg_weight_0.33_patch_weight_0.33_seed_100_step_312500.pt" BP2="final_weighted_False_autoregressive_False_distillation_False_teacher_True_mse_True_cosine_False_t_scale_True_swiglu_True_sigma_0.0_start_7_end_10_lr_0.0003_cls_weight_0.34_reg_weight_0.33_patch_weight_0.33_seed_101_step_312500.pt" BP3="final_weighted_True_autoregressive_False_distillation_False_teacher_True_mse_False_cosine_False_t_scale_True_swiglu_True_sigma_0.0_start_10_end_12_lr_0.0003_cls_weight_0.34_reg_weight_0.33_patch_weight_0.33_seed_104_step_312500.pt" python trainer.py --raptor3 --autoreg --weighted --sigma 0 --lr 3e-4 --wandb --t_scale --swiglu --start_layer 0 --end_layer 12 --cls_weight 0.45 --reg_weight 0.10 --patch_weight 0.45 --bp1 $BP1 --bp2 $BP2 --bp3 $BP3 --seed 1101 - Train linear probes on the frozen pretrained checkpoints.
cd src/imagenet_probes python train_probe.py --variant raptor3 --model_seed 1101 --seed 4005cd src/ade20k_probes python train_probe.py --variant raptor3 --model_seed 1101 --seed 5005cd src/nyud_probes python train_probe.py --variant raptor3 --model_seed 1101 --seed 6005
Reproducing Foundation Models Results (Section 3)
To reproduce the results for the foundation models section (Table 1 and Figure 7), do the following:
- Determine max-cut segmentations. This has been done for you in src/max_cut_dinov2_base.ipynb.
- Train each block independently.
cd src/runs sbatch blocks.sh - Train the full model with the pretrained blocks.
cd src/runs sbatch 002_raptor2_pretrained.sh sbatch 003_raptor3_pretrained.sh sbatch 004_raptor4_pretrained.sh - Train linear probes on the frozen pretrained checkpoints.
cd src/ade20k_probes sbatch run_all.shcd src/imagenet_probes sbatch run_all.shcd src/nyud_probes sbatch run_all.sh - Table 1
cd src python aggregate_results.py - Figure 7 Run the notebook in src/imagenet_probes/101_eval_error_bars.ipynb.