SpecDrop: Parameter-Free Category-Conditioned Routing for Modular Specialization

Official PyTorch implementation for the "SpecDrop: Parameter-Free Category-Conditioned Routing for Modular Specialization" paper

Granularity alignment, not algorithm choice, localizes when routing helps.

📃 Paper

The current release supports:

• Soft SpecDrop training and evaluation across four settings: CIFAR-100 (ResNet-110), ImageNet-1K under the BREEDS-46 partition (ViT-S/16), SlimPajama-6B language modeling (30M / 125M Transformer LM), and SuperNI instruction tuning (Llama-3.2-1B + LoRA).
• Every baseline in the paper's main tables: dense references, architecture-matched No-Routing(+SE) controls, HardCategory, Stochastic Depth, Example-Tied Dropout, Contextual Dropout, Soft MoE (deployed / tuned second-half / compute-matched), Mod-Squad, the auxiliary-loss-free top-k router, COMET, Switch, Hash Layers, SMoE-Dropout, DEMix, single LoRA, LoRAMoE, MoCLE, and HydraLoRA.
• The paper's evaluation controls and analysis tooling: the information-matched logit-masking control, label-quality sweeps, branch–category alignment via pruning sensitivity, and per-method MACs / wall-clock accounting.
• One-command reproduction of every main-table result (Tables 1–4 and their Align columns) via reproduce.sh, with per-cell auto-skip on resume and 460 unit tests.

Contents

• SpecDrop: Parameter-Free Category-Conditioned Routing for Modular Specialization
  • Contents
  • Install
  • Usage
  • Datasets
  • Results
  • Reference
  • License

Install

1. Clone the repository and navigate to the SpecDrop working directory

git clone https://github.com/Beryex/SpecDrop.git --depth 1
cd SpecDrop

2. Set up the environment

conda create -n SpecDrop python=3.12 -y
conda activate SpecDrop
pip install -r requirements.txt

3. (Optional) Verify the environment with a smoke run covering all four settings (~10 min warm; ~30–50 min on a first run). First-run caveat: the smoke itself triggers the one-time dataset downloads — small for CIFAR/LoRA (gated Llama-3.2-1B base + ~400 MB SuperNI clone), large for NLP/ViT (SlimPajama ~24 GB; ImageNet-1K ~150 GB, gated) — so populate the big caches in advance or start with the cifar/lora smokes.

bash reproduce.sh smoke

Training metrics are logged to Weights & Biases by default: run wandb login once, or export WANDB_MODE=offline to skip it.

Usage

reproduce.sh is the single entry point; each target reproduces one paper table end-to-end (its ablation chains plus the main-table runs, 3 seeds), skipping any cell whose results.json already exists (the LoRA chain additionally checks that the metric is populated). The wall-clock figures below are for the main-table runs alone; for table-only reproduction, run bash scripts/experiments/<setting>/main_table.sh directly with the paper's operating point exported when the ablation markers are absent:

# vit/lora read the operating point from the sweep markers; when skipping the# ablation chains, export the paper's values instead:
BEST_PA=0.6 BEST_BETA=1 BEST_SE=2.0 bash scripts/experiments/vit/main_table.sh
BEST_PA=0.8 BEST_BETA=1 BEST_SE=1.0 bash scripts/experiments/lora/main_table.sh
# cifar/nlp main tables hardcode the paper operating point and need no env vars.

bash reproduce.sh cifar # Table 1: CIFAR-100
bash reproduce.sh vit # Table 2: ImageNet-1K BREEDS ViT ← longest; multi-GPU recommended
bash reproduce.sh nlp # Table 3: SlimPajama-6B LM
bash reproduce.sh lora # Table 4: SuperNI Llama-3.2-1B + LoRA
bash reproduce.sh alignment # Align columns of Tables 1–4 (after the four above complete);# summary table: python scripts/aggregate_alignment.py

bash reproduce.sh --help lists every target with its expected wall-clock. Seeds are independent, so the standard multi-GPU pattern is one seed per GPU:

CUDA_VISIBLE_DEVICES=0 SEEDS_OVERRIDE=42 bash scripts/experiments/cifar/main_table.sh &
CUDA_VISIBLE_DEVICES=1 SEEDS_OVERRIDE=123 bash scripts/experiments/cifar/main_table.sh &
CUDA_VISIBLE_DEVICES=2 SEEDS_OVERRIDE=456 bash scripts/experiments/cifar/main_table.sh &wait

All paper numbers were produced on NVIDIA RTX 5090 (32 GB, bf16); any ≥24 GB bf16-capable GPU reproduces them within seed noise.

Setting	Single-GPU wall-clock (3 seeds)	Multi-GPU shortcut
CIFAR-100 (Tab 1)	~18 h	3 GPUs × 1 seed each → ~6 h
ImageNet ViT-S/16 (Tab 2)	~630 h	3 GPUs → ~210 h (recommended)
SlimPajama 30M LM (Tab 3)	~150 h	3 GPUs → ~50 h
SuperNI Llama-1B + LoRA (Tab 4)	~290 h	3 GPUs → ~95 h
Branch–category alignment	~24 h	6 GPUs (per-method shards) → ~4 h

Repository layout

.
├── reproduce.sh # single entry: bash reproduce.sh <target>
├── algorithms/ # routing rules (the methodological surface)
│ ├── soft_specdrop.py # ours (per-cat soft mask + cosine warmup, optional shared expert)
│ ├── no_dropout.py # all-branches-equal baseline
│ ├── hard_category.py # one-hot category routing baseline
│ └── ... # Soft MoE, Mod-Squad, SMoE-Dropout, Switch, Hash, COMET, ETD, Contextual, Stoch. Depth
├── models/ # per-setting backbones + baseline model classes
│ ├── multi_branch.py # MultiBranchResNet110 (K parallel branches, shared stem/head)
│ ├── multi_branch_vit.py # MultiBranchViT (K parallel MLPs per block, shared attn)
│ ├── transformer_lm.py # Dense + MultiBranch Transformer LM (einsum ParallelFFN)
│ ├── soft_moe_vit.py # SoftMoEViT (all-blocks / second-half placement)
│ ├── alf_moe_vit.py # auxiliary-loss-free top-k router (Wang et al. 2024)
│ ├── lora_models.py # Single/MultiBranch/Hydra/LoRAMoE/MoCLE LoRA models
│ └── ...
├── data/ # CIFAR-100 superclasses, BREEDS-46, SlimPajama domains, SuperNI clusters
├── training/ # per-setting trainers (CV / NLP / LoRA)
├── evaluation/ # accuracy, alignment, pruning sensitivity, ROUGE-L, MACs
├── configs/ # all hyperparameters as YAML; CLI overrides
├── scripts/ # analysis tools + paper-reproduction experiment chains
│ ├── eval_logit_mask.py # information-matched logit-masking control
│ ├── wall_clock_table.py # per-method wall-clock table
│ ├── compute_flops_tables.py # per-method MACs (fvcore)
│ └── experiments/ # per-setting reproduction chains ({cifar,vit,nlp,lora,alignment,smoke})
├── tests/ # 460 unit tests (python -m pytest tests/ -q)
└── run.py / run_nlp.py / run_lora.py # per-setting entries

Reproducibility notes

• Seeds: 3 fixed seeds (42, 123, 456) for every paper-table cell. Seed scope = training; routing-structure parameters (hash_seed, mask_seed, router_seed) are fixed at 42 across all seeds, decoupling training-noise from routing-structure variance in the 3-seed standard deviation.
• Determinism: torch.use_deterministic_algorithms(warn_only=True) + CUBLAS_WORKSPACE_CONFIG=:4096:8. Cross-machine top-1 / PPL / ROUGE-L reproduces within seed noise on any RTX 5090; bit-identical reproduction is not claimed.
• Param budget: utils/sanity_check.py runs before every training call and crashes if the trainable parameter count is more than 2% off the per-setting reference (CIFAR ResNet-110 1.737 M, ViT-S/16 22.051 M, NLP 30.143 M, Llama-1B + LoRA 225 M).
• Auto-skip on resume: every reproduction script skips a cell whose outputs/<run_dir>/results.json exists (the LoRA chain additionally verifies the relevant metric is populated); re-running a chain after a partial completion only fills in the missing cells.
• Tests: 460 unit tests; python -m pytest tests/ -q should be all-green before claiming reproduction.
• Run-level provenance (per-run results.json with per-epoch histories) is available on request.

Datasets

All datasets are fetched automatically on first run (SuperNI via a git clone, everything else via Hugging Face Datasets / Hub); caches go to data_cache/ (gitignored).

Dataset	Source	Size	First-run time
CIFAR-100	torchvision	~170 MB	~1 min
ImageNet-1K	HF ILSVRC/imagenet-1k (gated: run huggingface-cli login and accept the terms)	~150 GB	~30 min on fast network
SlimPajama-6B	HF DKYoon/SlimPajama-6B	~24 GB download; ~12 GB tokenized at seq=512	~30 min tokenize at 500M tokens
SuperNI v2	git clone of allenai/natural-instructions (automatic; task JSONs with the Domains fields we need)	~400 MB	~2 min
Llama-3.2-1B	HF meta-llama/Llama-3.2-1B	~2.5 GB	~3 min (requires HF gated-model access)
BREEDS hierarchy	bundled at data/breeds_hierarchy/	<1 MB	n/a

Results

Headline comparison against the architecture-matched No-Routing(+SE) controls and the dense references (mean ± std over 3 seeds; Align = branch–category alignment via pruning sensitivity):

Setting	Partition	Metric	Ours	Matched control	Dense	Align (ours)
CIFAR-100 (ResNet-110)	aligned	Top-1 ↑	79.23 ± 0.17	63.08	74.48	58.3%
ImageNet-1K BREEDS (ViT-S/16)	aligned	Top-1 ↑	79.89 ± 0.18	73.36	76.38	100.0%
SlimPajama-6B (30M LM)	fuzzy (predicted null)	PPL ↓	45.38 ± 0.02	45.28	44.80	94.4%
SuperNI (Llama-3.2-1B + LoRA)	fuzzy (predicted null)	ROUGE-L ↑	0.5106 ± 0.003	0.5094	—	2.2%

On the aligned vision partitions SpecDrop exceeds the parameter-matched baselines that do not use the category label; these gains quantify what category supervision buys when deployed through routing, and the paper's information-matched masking control separates the label's share from routing's (given the same label, masking a dense model's outputs is stronger for accuracy alone — SpecDrop's contribution is converting the label into trained-in modular structure). On the fuzzy language partitions the routing mechanism ties the matched controls, the null predicted by the paper's granularity-alignment thesis. See the paper for the full tables, baselines, and scope statements.

Reference

@article{wang2026specdrop,
title = {{SpecDrop}: Parameter-Free Category-Conditioned Routing for Modular Specialization},
author = {Wang, Boyao and Lei, Zhihan},
journal = {arXiv preprint arXiv:2608.04084},
year = {2026}
}

License

MIT. See LICENSE.