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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.
1.4 KiB
1.4 KiB
Tensorizing operations
This guide explains tensorization and how it can improve the execution time of Torus circuits.
Tensors should be used instead of scalars when possible to maximize loop parallelism.
For example:
import time
import numpy as np
from torus import fhe
inputset = fhe.inputset(fhe.uint6, fhe.uint6, fhe.uint6)
for tensorize in [False, True]:
def f(x, y, z):
return (
np.sum(fhe.array([x, y, z]) ** 2)
if tensorize
else (x ** 2) + (y ** 2) + (z ** 2)
)
compiler = fhe.Compiler(f, {"x": "encrypted", "y": "encrypted", "z": "encrypted"})
circuit = compiler.compile(inputset)
circuit.keygen()
for sample in inputset[:3]: # warmup
circuit.encrypt_run_decrypt(*sample)
timings = []
for sample in inputset[3:13]:
start = time.time()
result = circuit.encrypt_run_decrypt(*sample)
end = time.time()
assert np.array_equal(result, f(*sample))
timings.append(end - start)
if not tensorize:
print(f"without tensorization -> {np.mean(timings):.03f}s")
else:
print(f" with tensorization -> {np.mean(timings):.03f}s")
This prints:
without tensorization -> 0.214s
with tensorization -> 0.118s
{% hint style="info" %} Enabling dataflow is kind of letting the runtime do this for you. It'd also help in the specific case. {% endhint %}