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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.
190 lines
5.2 KiB
Python
190 lines
5.2 KiB
Python
"""
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Tests of execution of multivariate extension.
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"""
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import inspect
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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 torus.fhe.values import ValueDescription
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def x_if_y_else_zero(x, y):
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"""
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Multivariate function for keeping the value of x
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or setting it to 0, depending on the value of y.
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"""
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return np.where(y, x, 0)
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def multi(x, y, z):
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"""
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Multivariate function that results in multiple tables in the table lookup.
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"""
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result = x * y
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result[0] -= z
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result[2] += z
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return result
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cases = [
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[
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("x_if_y_else_zero", lambda x, y: fhe.multivariate(x_if_y_else_zero)(x, y)),
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[
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ValueDescription(
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Integer(is_signed=False, bit_width=3),
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shape=(),
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is_encrypted=True,
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),
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ValueDescription(
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Integer(is_signed=False, bit_width=1),
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shape=(),
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is_encrypted=True,
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),
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],
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fhe.MultivariateStrategy.CASTED,
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],
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[
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("x_if_y_else_zero", lambda x, y: fhe.multivariate(x_if_y_else_zero)(x, y)),
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[
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ValueDescription(
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Integer(is_signed=False, bit_width=3),
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shape=(2,),
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is_encrypted=True,
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),
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ValueDescription(
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Integer(is_signed=False, bit_width=1),
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shape=(),
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is_encrypted=True,
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),
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],
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fhe.MultivariateStrategy.PROMOTED,
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],
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[
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("x_if_y_else_zero", lambda x, y: fhe.multivariate(x_if_y_else_zero)(x, y)),
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[
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ValueDescription(
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Integer(is_signed=True, bit_width=3),
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shape=(),
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is_encrypted=True,
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),
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ValueDescription(
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Integer(is_signed=False, bit_width=1),
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shape=(2,),
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is_encrypted=True,
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),
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],
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fhe.MultivariateStrategy.CASTED,
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],
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[
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("x_if_y_else_zero", lambda x, y: fhe.multivariate(x_if_y_else_zero)(x, y)),
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[
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ValueDescription(
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Integer(is_signed=True, bit_width=3),
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shape=(2,),
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is_encrypted=True,
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),
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ValueDescription(
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Integer(is_signed=False, bit_width=1),
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shape=(2,),
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is_encrypted=True,
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),
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],
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fhe.MultivariateStrategy.PROMOTED,
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],
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[
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("multi", lambda x, y, z: fhe.multivariate(multi)(x, y, z)),
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[
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ValueDescription(
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Integer(is_signed=False, bit_width=2),
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shape=(3,),
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is_encrypted=True,
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),
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ValueDescription(
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Integer(is_signed=True, bit_width=2),
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shape=(3,),
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is_encrypted=True,
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),
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ValueDescription(
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Integer(is_signed=False, bit_width=2),
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shape=(),
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is_encrypted=True,
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),
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],
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fhe.MultivariateStrategy.CASTED,
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],
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[
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("multi", lambda x, y, z: fhe.multivariate(multi)(x, y, z)),
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[
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ValueDescription(
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Integer(is_signed=True, bit_width=2),
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shape=(3, 2),
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is_encrypted=True,
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),
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ValueDescription(
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Integer(is_signed=False, bit_width=2),
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shape=(1, 2),
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is_encrypted=True,
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),
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ValueDescription(
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Integer(is_signed=True, bit_width=2),
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shape=(2,),
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is_encrypted=True,
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),
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],
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fhe.MultivariateStrategy.PROMOTED,
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],
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]
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@pytest.mark.parametrize("operation,values,strategy", cases)
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def test_multivariate(operation, values, strategy, helpers):
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"""
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Test multivariate extension.
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"""
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name, function = operation
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print()
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print()
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print(
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f"{name}({', '.join(str(value) for value in values)})"
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+ (f" {{{strategy}}}" if strategy is not None else "")
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)
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print()
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print()
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signature = inspect.signature(function)
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parameters = list(signature.parameters.keys())
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parameter_encryption_statuses = {parameter: "encrypted" for parameter in parameters}
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configuration = helpers.configuration()
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if strategy is not None:
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configuration = configuration.fork(multivariate_strategy_preference=strategy)
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compiler = fhe.Compiler(function, parameter_encryption_statuses)
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inputset = [
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tuple(
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np.random.randint(value.dtype.min(), value.dtype.max() + 1, size=value.shape)
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for value in values
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)
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for _ in range(100)
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]
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circuit = compiler.compile(inputset, configuration)
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samples = [
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[np.zeros(value.shape, dtype=np.int64) for value in values],
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[np.ones(value.shape, dtype=np.int64) * value.dtype.max() for value in values],
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[
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np.random.randint(value.dtype.min(), value.dtype.max() + 1, size=value.shape)
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for value in values
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],
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]
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for sample in samples:
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helpers.check_execution(circuit, function, sample, retries=5)
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