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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.
2.3 KiB
2.3 KiB
Optimize table lookups
This guide teaches how costly table lookups are, and how to optimize them to improve the execution time of Torus circuits.
The most costly operation in Torus is the table lookup operation, so one of the primary goals of optimizing performance is to reduce the amount of table lookups.
Furthermore, the bit width of the input of the table lookup plays a major role in performance.
import time
import numpy as np
import matplotlib.pyplot as plt
from torus import fhe
def f(x):
return x // 2
bit_widths = list(range(2, 9))
complexities = []
timings = []
for bit_width in bit_widths:
inputset = fhe.inputset(lambda _: np.random.randint(0, 2 ** bit_width))
compiler = fhe.Compiler(f, {"x": "encrypted"})
circuit = compiler.compile(inputset)
circuit.keygen()
for sample in inputset[:3]: # warmup
circuit.encrypt_run_decrypt(*sample)
current_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))
current_timings.append(end - start)
complexities.append(int(circuit.complexity))
timings.append(float(np.mean(current_timings)))
print(f"{bit_width} bits -> {complexities[-1]:>13_} complexity -> {timings[-1]:.06f}s")
figure, complexity_axis = plt.subplots()
color = "tab:red"
complexity_axis.set_xlabel("bit width")
complexity_axis.set_ylabel("complexity", color=color)
complexity_axis.plot(bit_widths, complexities, color=color)
complexity_axis.tick_params(axis="y", labelcolor=color)
timing_axis = complexity_axis.twinx()
color = 'tab:blue'
timing_axis.set_ylabel('execution time', color=color)
timing_axis.plot(bit_widths, timings, color=color)
timing_axis.tick_params(axis='y', labelcolor=color)
figure.tight_layout()
plt.show()
The code above prints:
2 bits -> 29_944_416 complexity -> 0.019826s
3 bits -> 42_154_798 complexity -> 0.020093s
4 bits -> 61_979_934 complexity -> 0.021961s
5 bits -> 99_198_195 complexity -> 0.029475s
6 bits -> 230_210_706 complexity -> 0.062841s
7 bits -> 535_706_740 complexity -> 0.139669s
8 bits -> 1_217_510_420 complexity -> 0.318838s
And displays:
