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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.
86 lines
2.3 KiB
Python
86 lines
2.3 KiB
Python
"""
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Tests of execution of dot operation.
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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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"size",
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[1, 4, 6, 10],
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)
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def test_constant_dot(size, helpers):
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"""
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Test dot where one of the operators is a constant.
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"""
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configuration = helpers.configuration()
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bound = int(np.floor(np.sqrt(127 / size)))
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cst = np.random.randint(0, bound, size=(size,))
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@fhe.compiler({"x": "encrypted"})
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def left_function(x):
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return np.dot(x, cst)
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@fhe.compiler({"x": "encrypted"})
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def right_function(x):
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return np.dot(cst, x)
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@fhe.compiler({"x": "encrypted"})
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def method(x):
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return x.dot(cst)
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inputset = [np.random.randint(0, bound, size=(size,)) for i in range(100)]
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left_function_circuit = left_function.compile(inputset, configuration)
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right_function_circuit = right_function.compile(inputset, configuration)
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method_circuit = method.compile(inputset, configuration)
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sample = np.random.randint(0, bound, size=(size,))
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helpers.check_execution(left_function_circuit, left_function, sample)
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helpers.check_execution(right_function_circuit, right_function, sample)
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helpers.check_execution(method_circuit, method, sample)
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@pytest.mark.parametrize("size", [1, 10])
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@pytest.mark.parametrize("bit_width", [2, 6])
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@pytest.mark.parametrize("signed", [True, False])
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@pytest.mark.parametrize("only_negative", [True, False])
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def test_dot(size, bit_width, only_negative, signed, helpers):
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"""
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Test dot.
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"""
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configuration = helpers.configuration()
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minimum = 0 if not signed else -(2 ** (bit_width - 1))
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maximum = 2**bit_width if not signed else 2 ** (bit_width - 1)
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if only_negative:
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maximum = 1
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@fhe.compiler({"x": "encrypted", "y": "encrypted"})
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def function(x, y):
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return np.dot(x, y)
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inputset = [
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(
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np.random.randint(minimum, maximum, size=(size,)),
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np.random.randint(minimum, maximum, size=(size,)),
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)
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for i in range(100)
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]
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circuit = function.compile(inputset, configuration)
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sample = [
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np.random.randint(minimum, maximum, size=(size,)),
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np.random.randint(minimum, maximum, size=(size,)),
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]
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helpers.check_execution(circuit, function, sample, retries=3)
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