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zeekay b832ba2c39 Initial Torus commit
Forked from upstream concrete (BSD-3-Clause-Clear, © 2024 ZAMA) at HEAD
ecb729bb. Modifications copyright © 2026 Lux Industries Inc.

Rebranding scope:
  - Python module concrete → torus (across frontends/torus-python)
  - Rust module concrete-* → torus-* (across frontends/torus-rust)
  - MLIR dialect concretelang → toruslang (across compilers/torus-compiler)
  - Compiler optimizer concrete-optimizer → torus-optimizer
  - Backend concrete-cpu / concrete-cuda → torus-cpu / torus-cuda
  - Wire protocol concrete-protocol → torus-protocol
  - Parameter-curves concrete-security-curves-* → torus-security-curves-*
  - Public symbols concretelib → toruslib, ConcreteLang → TorusLang
  - URLs github.com/zama-ai/concrete → github.com/luxfhe/torus
  - URLs docs.zama.ai/concrete → docs.luxfhe.com/torus

Legal compliance:
  - LICENSE.txt retains original Copyright © 2024 ZAMA verbatim (§1-§2)
  - LICENSE.txt adds modifications copyright © 2026 Lux Industries Inc.
  - §3 endorsement clause: Torus is marketed exclusively as a Lux product;
    no Zama name used in README, docs, or marketing
  - NOTICE file provides factual attribution per legal best practice
    (not endorsement; required by upstream BSD-3-Clause-Clear)

README + NOTICE rewritten from scratch with Lux brand:
  - Quick-start in Python (torus.fhe)
  - Architecture diagram showing the gen-1 vtbl integration path
    (luxcpp/gpu → lux-private/gpu-kernels for CUDA/Metal/WebGPU)
  - Scheme coverage table: TFHE (luxfi/fhe), CKKS/BGV/BFV (luxfi/lattice)
  - Lux FHE family table: Torus / TorusVM / TorusEVM / TorusML / TorusNet

Known limitations (phase-2A scope):
  - PyPI package torus-fhe not yet published; the torus Python module
    is renamed structurally but pip-installable distribution is a
    follow-up (Phase 2A.1)
  - MLIR dialect rename is text-level; the compiler-internal LLVM
    rebuild (concrete-compiler-internal-llvm-project submodule) is
    structural and works; tablegen files renamed concrete→torus but the
    generated .inc files in the source tree will regenerate at build
    time
  - backends/torus-cuda/implementation/ is an upstream submodule
    pointer to the tfhe-rs source (not fetched in the staging clone);
    the CUDA backend body is delivered out-of-tree
  - ~100 test-fixture variable names like concrete_encoded_sample
    retain "concrete" as a math term (vs symbolic); these are inside
    tests/, not the public API; cleanup is a follow-up commit
  - 295 Python `from concrete import ...` residuals in luxfhe/ml-sdk
    consumer remain; that repo's import update is Phase 2B

This commit is the seed for the luxfhe/torus repo. Push as the first
commit to a fresh GitHub repo; do not re-import upstream Zama git
history.
2026-05-29 14:16:47 -07:00

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Reuse Arguments

This document explains how to reuse encrypted arguments in applications where the same arguments are used repeatedly.

Encrypting data can be resource-intensive, especially when the same argument or set of arguments is used multiple times. In such cases, it’s inefficient to encrypt and transfer the arguments repeatedly. Instead, you can encrypt the arguments separately and reuse them as needed. By encrypting the arguments once and reusing them, you can optimize performance by reducing encryption time, memory usage, and network bandwidth.

Here is an example:

from torus import fhe

@fhe.compiler({"x": "encrypted", "y": "encrypted"})
def add(x, y):
    return x + y

inputset = [(2, 3), (0, 0), (1, 6), (7, 7), (7, 1), (3, 2), (6, 1), (1, 7), (4, 5), (5, 4)]
circuit = add.compile(inputset)

sample_y = 4
_, encrypted_y = circuit.encrypt(None, sample_y)

for sample_x in range(3, 6):
    encrypted_x, _ = circuit.encrypt(sample_x, None)

    encrypted_result = circuit.run(encrypted_x, encrypted_y)
    result = circuit.decrypt(encrypted_result)

    assert result == sample_x + sample_y

Note when you use encrypt method:

  • If you have multiple arguments, the encrypt method would return a tuple.
  • If you specify None as one of the arguments, None is placed at the same location in the resulting tuple.
    • For example, circuit.encrypt(a, None, b, c, None) returns (encrypted_a, None, encrypted_b, encrypted_c, None).
  • Each value returned by encrypt can be stored and reused anytime.

{% hint style="warning" %} The order of arguments must be consistent when encrypting and using them. Encrypting an x and using it as a y could result in undefined behavior. {% endhint %}