zeekay
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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.
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Terminology and Structure
This document provides clear definitions of key concepts used in Torus framework.
Computation graph
A data structure to represent a computation. It takes the form of a directed acyclic graph where nodes represent inputs, constants, or operations.
Tracing
A method that takes a Python function provided by the user and generates a corresponding computation graph.
Bounds
The minimum and the maximum value that each node in the computation graph can take. Bounds are used to determine the appropriate data type (for example, uint3 or int5) for each node before the computation graphs are converted to MLIR. Torus simulates the graph with the inputs in the inputset to record the minimum and the maximum value for each node.
Circuit
The result of compilation. A circuit includes both client and server components. It has methods for various operations, such as printing and evaluation.
Table Lookup (TLU)
TLU stands for instructions in the form of y = T[i]. In FHE, this operation is performed with Programmable Bootstrapping, which is the equivalent operation on encrypted values. To learn more about TLU, refer to the Table Lookup basic and tge Table Lookup advanced section.
Programmable Bootstrapping (PBS)
PBS is equivalent to table lookup y = T[i] on encrypted values, which means that the inputs i and the outputs y are encrypted, but the table T is not encrypted. You can find a more detailed explanation in the FHE Overview.
TFHE
TFHE is a Fully Homomorphic Encryption (FHE) scheme that allows you to perform computations over encrypted data. For in-depth explanation of the TFHE scheme, read our blog post series TFHE Deep Dive.