do-transformers-need-3-projections

Do Transformers Need Three Projections? — checkpoints

Reference checkpoints from the ICML 2026 paper "Do Transformers Need Three Projections? A Systematic Study of QKV Variants" (Kayyam, Madan Gopal, Lewis; BrainChip Inc.). This release pairs the standard QKV baseline with the paper's headline Q≠K=V variant so the two can be compared directly on matched architectures.

• QKV — standard attention with separate query, key, and value projections (baseline).
• Q≠K=V — separate query, shared key and value (V = K). Only K is cached during autoregressive generation → 50% KV-cache reduction, while attention stays asymmetric (unlike the symmetric Q=K variants).

The point of the pairing: Q≠K=V matches the QKV baseline in accuracy at half the KV cache. The full study (all six projection-sharing variants) is in the code repository.

What's included

18 trained-from-scratch checkpoints — QKV (*_qkv.pt) and Q≠K=V (*_qkv_kv.pt) for each task. Each is a dict: {task, variant, model, config, test_accuracy, model_state_dict}.

Synthetic sequence tasks (single Transformer encoder, token accuracy):

Files: checkpoints/synthetic_<task>_qkv.pt and checkpoints/synthetic_<task>_qkv_kv.pt.

Vision classification (ViT trained from scratch, top-1 accuracy):

Files: checkpoints/vision_<dataset>_qkv.pt and checkpoints/vision_<dataset>_qkv_kv.pt.

Across every task the two variants are within ~0.005 of each other — Q≠K=V matches the QKV baseline while halving the KV cache.

Usage

The model definitions live in the GitHub repository. Clone it, then load a checkpoint:

git clone https://github.com/Brainchip-Inc/Do-Transformers-Need-3-Projections
pip install torch torchvision huggingface_hub

import torch
from huggingface_hub import hf_hub_download

REPO = "BrainChip-AI/do-transformers-need-3-projections"

# --- synthetic task model (swap _qkv <-> _qkv_kv for baseline vs Q!=K=V) ---
from synthetic_tasks import Encoder, ModelCfg
path = hf_hub_download(REPO, "checkpoints/synthetic_reverse_qkv_kv.pt")
ckpt = torch.load(path, map_location="cpu")
model = Encoder(ModelCfg(**ckpt["config"]))
model.load_state_dict(ckpt["model_state_dict"]); model.eval()

# --- vision (ViT) model ---
from vision_tasks import ViT, ViTConfig
path = hf_hub_download(REPO, "checkpoints/vision_cifar10_qkv_kv.pt")
ckpt = torch.load(path, map_location="cpu")
vit = ViT(ViTConfig(**ckpt["config"]))
vit.load_state_dict(ckpt["model_state_dict"]); vit.eval()

Training

• Synthetic: single encoder, embedding dim 256, 2 layers, 4 heads, sequence length 64, one-hot inputs, Adam (lr 1e-3), cross-entropy, gradient clip 5, 10 epochs.
• Vision: ViT, patch 4, embedding dim 256, 2 layers, 4 heads, Adam (lr 1e-3) with MultiStepLR, cross-entropy; 20 epochs (MNIST/FMNIST), 40 (CIFAR-10), 50 (CIFAR-100); CIFAR uses random-crop + horizontal-flip augmentation. Trained from scratch on a single NVIDIA GTX 1080 Ti.

These are reproduction checkpoints; absolute numbers may differ slightly from the paper's tables (training-budget and augmentation details). See the repository for the full methodology and all six attention variants.

Citation

@inproceedings{kayyam2026qkv,
  title={Do Transformers Need Three Projections? A Systematic Study of {QKV} Variants},
  author={Kayyam, Ali and Madan Gopal, Anusha and Lewis, M Anthony},
  booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year={2026},
  series={PMLR},
  volume={306}
}

License

MIT (see the code repository).