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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.
220 lines
6.8 KiB
Python
220 lines
6.8 KiB
Python
"""
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Tests of execution of mul 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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"function",
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[
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pytest.param(
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lambda x: x * 3,
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id="x * 3",
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),
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pytest.param(
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lambda x: 3 * x,
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id="3 * x",
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),
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pytest.param(
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lambda x: np.dot(x, 3),
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id="np.dot(x, 3)",
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),
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pytest.param(
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lambda x: np.dot(3, x),
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id="np.dot(3, x)",
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),
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pytest.param(
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lambda x: x * np.array([1, 2, 3]),
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id="x * [1, 2, 3]",
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),
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pytest.param(
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lambda x: np.array([1, 2, 3]) * x,
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id="[1, 2, 3] * x",
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),
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pytest.param(
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lambda x: x * np.array([[1, 2, 3], [3, 1, 2]]),
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id="x * [[1, 2, 3], [3, 1, 2]]",
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),
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pytest.param(
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lambda x: np.array([[1, 2, 3], [3, 1, 2]]) * x,
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id="[[1, 2, 3], [3, 1, 2]] * x",
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),
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],
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)
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@pytest.mark.parametrize(
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"parameters",
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[
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{
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"x": {"range": [0, 40], "status": "encrypted"},
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},
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{
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"x": {"range": [0, 40], "status": "encrypted", "shape": (3,)},
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},
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{
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"x": {"range": [0, 40], "status": "encrypted", "shape": (2, 3)},
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},
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],
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)
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def test_constant_mul(function, parameters, helpers):
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"""
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Test mul where one of the operators is a constant.
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"""
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parameter_encryption_statuses = helpers.generate_encryption_statuses(parameters)
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configuration = helpers.configuration()
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compiler = fhe.Compiler(function, parameter_encryption_statuses)
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inputset = helpers.generate_inputset(parameters)
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circuit = compiler.compile(inputset, configuration)
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sample = helpers.generate_sample(parameters)
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helpers.check_execution(circuit, function, sample)
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@pytest.mark.parametrize(
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"function",
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[
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pytest.param(
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lambda x, y: x * y,
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id="x * y",
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),
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],
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)
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@pytest.mark.parametrize(
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"parameters",
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[
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{
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"x": {"range": [0, 10], "status": "clear"},
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"y": {"range": [0, 10], "status": "encrypted"},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted"},
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"y": {"range": [0, 10], "status": "clear"},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted"},
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"y": {"range": [0, 10], "status": "encrypted"},
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},
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{
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"x": {"range": [0, 10], "status": "clear", "shape": (3,)},
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"y": {"range": [0, 10], "status": "encrypted"},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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"y": {"range": [0, 10], "status": "clear"},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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"y": {"range": [0, 10], "status": "encrypted"},
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},
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{
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"x": {"range": [0, 10], "status": "clear"},
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"y": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted"},
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"y": {"range": [0, 10], "status": "clear", "shape": (3,)},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted"},
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"y": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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},
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{
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"x": {"range": [0, 10], "status": "clear", "shape": (3,)},
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"y": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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"y": {"range": [0, 10], "status": "clear", "shape": (3,)},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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"y": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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},
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{
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"x": {"range": [0, 10], "status": "clear", "shape": (2, 1)},
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"y": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted", "shape": (2, 1)},
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"y": {"range": [0, 10], "status": "clear", "shape": (3,)},
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},
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{
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"x": {"range": [0, 10], "status": "encrypted", "shape": (2, 1)},
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"y": {"range": [0, 10], "status": "encrypted", "shape": (3,)},
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},
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{
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"x": {"range": [-10, 10], "status": "encrypted", "shape": (3, 2)},
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"y": {"range": [-10, 10], "status": "encrypted", "shape": (3, 2)},
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},
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{
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"x": {"range": [-10, 10], "status": "encrypted", "shape": (3, 1)},
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"y": {"range": [0, 0], "status": "encrypted", "shape": (3, 1)},
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},
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{
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"x": {"range": [10, 20], "status": "encrypted", "shape": (3, 1)},
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"y": {"range": [0, 0], "status": "encrypted", "shape": (1, 3)},
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},
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{
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"x": {"range": [2**12, 2**13 - 1], "status": "encrypted", "shape": (3, 1)},
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"y": {"range": [0, 0], "status": "encrypted", "shape": (1, 3)},
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},
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{
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"x": {"range": [2**12, 2**13 - 1], "status": "encrypted", "shape": (3, 1)},
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"y": {"range": [0, 2 * 3 - 1], "status": "encrypted", "shape": (1, 3)},
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},
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{
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"x": {"range": [-(2**7), 2**7 - 1], "status": "encrypted", "shape": (3, 1)},
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"y": {"range": [-(2**7), 2**7 - 1], "status": "encrypted", "shape": (1, 3)},
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},
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],
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)
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@pytest.mark.minimal
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def test_mul(function, parameters, helpers):
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"""
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Test mul where both of the operators are dynamic.
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"""
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parameter_encryption_statuses = helpers.generate_encryption_statuses(parameters)
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configuration = helpers.configuration()
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compiler = fhe.Compiler(function, parameter_encryption_statuses)
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inputset = helpers.generate_inputset(parameters)
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circuit = compiler.compile(inputset, configuration)
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sample = helpers.generate_sample(parameters)
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helpers.check_execution(circuit, function, sample, retries=3)
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@pytest.mark.parametrize(
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"parameter_encryption_statuses,function,inputs",
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[
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pytest.param(
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{"x": "encrypted", "y": "encrypted"},
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lambda x, y: (x - y) * ((x - y) > 0),
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[
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np.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]),
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np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]),
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],
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id="(x - y) * ((x - y) > 0)",
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),
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],
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)
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def test_mul_specific(parameter_encryption_statuses, function, inputs, helpers):
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"""
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Test mul with specific inputs.
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"""
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configuration = helpers.configuration()
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compiler = fhe.Compiler(function, parameter_encryption_statuses)
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circuit = compiler.compile([tuple(inputs)], configuration)
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helpers.check_execution(circuit, function, inputs)
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