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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.
111 lines
3.0 KiB
Python
111 lines
3.0 KiB
Python
"""
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Tests of execution of dynamic tlu operation.
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"""
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import random
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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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from torus.fhe.dtypes import Integer
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from ..conftest import USE_MULTI_PRECISION
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cases = []
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for input_bit_width in range(1, 3):
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for input_is_signed in [False, True]:
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for output_bit_width in range(1, 3):
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for output_is_signed in [False, True]:
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input_shape = random.choice([(), (2,), (3, 2)])
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cases.append(
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pytest.param(
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input_bit_width,
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input_is_signed,
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input_shape,
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output_bit_width,
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output_is_signed,
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id=(
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f"{'' if input_is_signed else 'u'}int{input_bit_width}"
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f" -> "
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f"{'' if output_is_signed else 'u'}int{output_bit_width}"
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f" {{ input_shape={input_shape} }}"
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),
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)
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)
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# pylint: disable=redefined-outer-name
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@pytest.mark.parametrize(
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"input_bit_width,input_is_signed,input_shape,output_bit_width,output_is_signed",
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cases,
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)
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def test_dynamic_tlu(
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input_bit_width,
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input_is_signed,
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input_shape,
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output_bit_width,
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output_is_signed,
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helpers,
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):
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"""
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Test dynamic tlu.
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"""
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input_dtype = Integer(is_signed=input_is_signed, bit_width=input_bit_width)
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output_dtype = Integer(is_signed=output_is_signed, bit_width=output_bit_width)
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def function(x, y):
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return y[x]
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compiler = fhe.Compiler(function, {"x": "encrypted", "y": "clear"})
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inputset = [
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(
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np.random.randint(
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input_dtype.min(),
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input_dtype.max() + 1,
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size=input_shape,
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),
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np.random.randint(
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output_dtype.min(),
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output_dtype.max() + 1,
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size=(
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2
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** (
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input_bit_width
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if USE_MULTI_PRECISION
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else max(input_bit_width, output_bit_width)
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),
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),
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),
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)
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for _ in range(100)
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]
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circuit = compiler.compile(inputset, helpers.configuration())
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samples = [
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[
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np.random.randint(
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input_dtype.min(),
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input_dtype.max() + 1,
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size=input_shape,
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),
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np.random.randint(
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output_dtype.min(),
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output_dtype.max() + 1,
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size=(
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2
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** (
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input_bit_width
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if USE_MULTI_PRECISION
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else max(input_bit_width, output_bit_width)
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),
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),
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),
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]
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for _ in range(5)
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]
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for sample in samples:
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helpers.check_execution(circuit, function, sample, retries=3)
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