supersayan 0.1.2

A high-performance Python library for fully homomorphic encryption (FHE) in deep learning, seamlessly integrated with PyTorch.

Supersayan

A high‑performance Python library that integrates Fully Homomorphic Encryption (FHE) into PyTorch workflows. Supersayan provides:

• A client/server path to offload selected layers to a remote FHE server (hybrid mode).
• Zero‑copy tensor bridges across PyTorch, NumPy, CuPy, and Julia.

The Julia backend is managed via juliacall and initialized automatically on first import, or explicitly with a helper CLI.

Installation

From PyPI:

pip install supersayan
# or with uv
uv add supersayan

Default behavior:

• After install, importing supersayan triggers a one‑time Julia backend setup automatically. No extra step is needed for typical users.

Optional (CI/Docker or troubleshooting):

# Manually initialize the Julia backend if you want explicit control
supersayan-setup

Advanced control (not recommended for regular users):

• To prevent network access during import in CI images, set SUPERSAYAN_SKIP_JULIA_SETUP=1 and run supersayan-setup in a controlled build step.

Notes:

• GPU support uses CuPy when available; code runs on CPU otherwise.

Hybrid Remote Inference

Run the TCP server:

python scripts/run_server.py --host 127.0.0.1 --port 8000 --models-dir /tmp/supersayan/models

Use the client to execute only selected layers remotely in FHE while keeping other ops local:

import torch
import torch.nn as nn
from supersayan.core.types import SupersayanTensor
from supersayan.remote import SupersayanClient

class SmallCNN(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = nn.Conv2d(1, 4, 3, padding=1)
        self.fc = nn.Linear(4*28*28, 10)
    def forward(self, x):
        x = torch.relu(self.conv(x))
        return self.fc(x.view(x.size(0), -1))

cnn = SmallCNN().eval()
client = SupersayanClient(
    server_url="127.0.0.1:8000",
    torch_model=cnn,
    fhe_modules=[nn.Conv2d, nn.Linear],  # offload these layers
)

x = SupersayanTensor(torch.randn(1, 1, 28, 28))
y = client(x)

For a runnable example of the TCP server, see scripts/run_server.py.

Supported offloaded layers: nn.Linear, nn.Conv2d.

Tensors and Interop

• SupersayanTensor(data, device=...) accepts torch.Tensor, numpy.ndarray, or cupy.ndarray and preserves dtype float32.
• Helpers: SupersayanTensor.zeros(...), ones(...), randn(...).
• Interop: .to_numpy(), .to_dlpack(), and zero‑copy conversion to Julia via .to_julia().

Project Layout

• src/supersayan/ core, layers, remote client/server, Julia backend
• scripts/ runnable examples (run_server.py)

Troubleshooting

• If Julia setup fails on first import, run supersayan-setup manually.
• In CI or headless environments, set SUPERSAYAN_SKIP_JULIA_SETUP=1 during import and run supersayan-setup explicitly in a build step.