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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.
94 lines
2.3 KiB
Python
94 lines
2.3 KiB
Python
"""
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Tests of compilation utilities.
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"""
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import numpy as np
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import pytest
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from torus import fhe
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@pytest.mark.parametrize(
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"inputs,ranges,shapes",
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[
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(
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(fhe.uint3,), # type: ignore
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([0, 2**3],),
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((),),
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),
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(
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(fhe.int3,),
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([-(2**2), 2**2],), # type: ignore
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((),),
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),
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(
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(fhe.tensor[fhe.uint3, 3, 2],), # type: ignore
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([0, 2**3],),
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((3, 2),),
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),
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(
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(fhe.tensor[fhe.int3, 3, 2],), # type: ignore
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([-(2**2), 2**2],),
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((3, 2),),
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),
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(
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(fhe.uint3, fhe.uint4), # type: ignore
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([0, 2**3], [0, 2**4]),
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((), ()),
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),
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(
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(fhe.int4, fhe.int3), # type: ignore
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([-(2**3), 2**3], [-(2**2), 2**2]),
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((), ()),
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),
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(
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(fhe.tensor[fhe.uint6, 3, 2], fhe.tensor[fhe.int6, 5]), # type: ignore
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([0, 2**6], [-(2**5), 2**5]),
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((3, 2), (5,)),
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),
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(
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(fhe.f32,), # type: ignore
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([0.0, 1.0],),
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((),),
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),
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(
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(fhe.tensor[fhe.f32, 3, 2],), # type: ignore
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([0.0, 1.0],),
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((3, 2),),
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),
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(
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(lambda _index: np.random.randint(10, 20),),
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([10, 20],),
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((),),
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),
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(
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(lambda _index: np.random.randint(10, 20, size=(3, 2)),),
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([10, 20],),
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((3, 2),),
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),
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],
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)
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@pytest.mark.parametrize("size", [10, 100])
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def test_inputset(inputs, ranges, shapes, size):
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"""
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Test `inputset` utility.
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"""
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inputset = fhe.inputset(*inputs, size=size)
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assert isinstance(inputset, list)
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assert len(inputset) == size
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for sample in inputset:
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assert isinstance(sample, tuple)
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assert len(sample) == len(inputs)
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for value, range_, shape in zip(sample, ranges, shapes):
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assert isinstance(value, (int, float, np.ndarray))
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if isinstance(value, (int, float)):
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assert shape == ()
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assert range_[0] <= value < range_[1]
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else:
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assert shape == value.shape
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assert value.min() >= range_[0]
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assert value.max() < range_[1]
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