mirror of
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zeekay
b832ba2c39
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.
328 lines
8.4 KiB
Python
328 lines
8.4 KiB
Python
"""
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Tests of `Value` class.
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"""
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import numpy as np
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import pytest
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from torus.fhe.dtypes import Float, SignedInteger, UnsignedInteger
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from torus.fhe.values import (
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ClearScalar,
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ClearTensor,
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EncryptedScalar,
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EncryptedTensor,
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ValueDescription,
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)
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@pytest.mark.parametrize(
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"value,is_encrypted,expected_result",
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[
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pytest.param(
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True,
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True,
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ValueDescription(dtype=UnsignedInteger(1), shape=(), is_encrypted=True),
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),
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pytest.param(
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True,
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False,
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ValueDescription(dtype=UnsignedInteger(1), shape=(), is_encrypted=False),
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),
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pytest.param(
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False,
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True,
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ValueDescription(dtype=UnsignedInteger(1), shape=(), is_encrypted=True),
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),
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pytest.param(
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False,
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False,
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ValueDescription(dtype=UnsignedInteger(1), shape=(), is_encrypted=False),
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),
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pytest.param(
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0,
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False,
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ValueDescription(dtype=UnsignedInteger(1), shape=(), is_encrypted=False),
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),
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pytest.param(
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np.int32(0),
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True,
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ValueDescription(dtype=UnsignedInteger(1), shape=(), is_encrypted=True),
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),
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pytest.param(
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0.0,
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False,
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ValueDescription(dtype=Float(64), shape=(), is_encrypted=False),
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),
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pytest.param(
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np.float64(0.0),
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True,
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ValueDescription(dtype=Float(64), shape=(), is_encrypted=True),
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),
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pytest.param(
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np.float32(0.0),
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False,
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ValueDescription(dtype=Float(32), shape=(), is_encrypted=False),
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),
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pytest.param(
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np.float16(0.0),
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True,
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ValueDescription(dtype=Float(16), shape=(), is_encrypted=True),
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),
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pytest.param(
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[True, False, True],
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False,
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ValueDescription(dtype=UnsignedInteger(1), shape=(3,), is_encrypted=False),
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),
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pytest.param(
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[True, False, True],
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True,
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ValueDescription(dtype=UnsignedInteger(1), shape=(3,), is_encrypted=True),
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),
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pytest.param(
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[0, 3, 1, 2],
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False,
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ValueDescription(dtype=UnsignedInteger(2), shape=(4,), is_encrypted=False),
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),
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pytest.param(
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np.array([0, 3, 1, 2], dtype=np.int32),
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True,
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ValueDescription(dtype=UnsignedInteger(2), shape=(4,), is_encrypted=True),
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),
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pytest.param(
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np.array([0.2, 3.4, 1.5, 2.0], dtype=np.float64),
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False,
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ValueDescription(dtype=Float(64), shape=(4,), is_encrypted=False),
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),
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pytest.param(
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np.array([0.2, 3.4, 1.5, 2.0], dtype=np.float32),
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True,
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ValueDescription(dtype=Float(32), shape=(4,), is_encrypted=True),
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),
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pytest.param(
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np.array([0.2, 3.4, 1.5, 2.0], dtype=np.float16),
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False,
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ValueDescription(dtype=Float(16), shape=(4,), is_encrypted=False),
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),
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],
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)
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def test_value_of(value, is_encrypted, expected_result):
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"""
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Test `of` function of `Value` class.
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"""
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assert ValueDescription.of(value, is_encrypted) == expected_result
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@pytest.mark.parametrize(
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"value,is_encrypted,expected_error,expected_message",
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[
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pytest.param(
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"abc",
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False,
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ValueError,
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"Torus cannot represent 'abc'",
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),
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pytest.param(
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[1, (), 3],
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False,
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ValueError,
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"Torus cannot represent [1, (), 3]",
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),
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],
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)
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def test_value_bad_of(value, is_encrypted, expected_error, expected_message):
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"""
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Test `of` function of `Value` class with bad parameters.
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"""
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with pytest.raises(expected_error) as excinfo:
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ValueDescription.of(value, is_encrypted)
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assert str(excinfo.value) == expected_message
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@pytest.mark.parametrize(
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"lhs,rhs,expected_result",
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[
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pytest.param(
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ClearScalar(SignedInteger(5)),
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ValueDescription(dtype=SignedInteger(5), shape=(), is_encrypted=False),
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True,
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),
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pytest.param(
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ClearTensor(UnsignedInteger(5), shape=(3, 2)),
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ValueDescription(dtype=UnsignedInteger(5), shape=(3, 2), is_encrypted=False),
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True,
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),
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pytest.param(
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EncryptedScalar(SignedInteger(5)),
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ValueDescription(dtype=SignedInteger(5), shape=(), is_encrypted=True),
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True,
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),
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pytest.param(
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EncryptedTensor(UnsignedInteger(5), shape=(3, 2)),
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ValueDescription(dtype=UnsignedInteger(5), shape=(3, 2), is_encrypted=True),
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True,
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),
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pytest.param(
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EncryptedTensor(UnsignedInteger(5), shape=(3, 2)),
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ClearScalar(SignedInteger(3)),
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False,
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),
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pytest.param(
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ClearScalar(SignedInteger(3)),
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"ClearScalar(SignedInteger(3))",
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False,
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),
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pytest.param(
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"ClearScalar(SignedInteger(3))",
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ClearScalar(SignedInteger(3)),
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False,
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),
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],
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)
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def test_value_eq(lhs, rhs, expected_result):
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"""
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Test `__eq__` method of `Value` class.
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"""
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assert (lhs == rhs) == expected_result
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assert (rhs == lhs) == expected_result
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@pytest.mark.parametrize(
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"data_type,expected_result",
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[
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pytest.param(
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ClearScalar(SignedInteger(5)),
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"ClearScalar<int5>",
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),
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pytest.param(
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EncryptedScalar(SignedInteger(5)),
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"EncryptedScalar<int5>",
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),
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pytest.param(
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ClearTensor(SignedInteger(5), shape=(3, 2)),
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"ClearTensor<int5, shape=(3, 2)>",
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(3, 2)),
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"EncryptedTensor<int5, shape=(3, 2)>",
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),
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],
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)
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def test_value_str(data_type, expected_result):
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"""
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Test `__str__` method of `Value` class.
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"""
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assert str(data_type) == expected_result
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@pytest.mark.parametrize(
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"value,expected_result",
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[
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pytest.param(
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ClearScalar(SignedInteger(5)),
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True,
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),
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pytest.param(
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EncryptedScalar(SignedInteger(5)),
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False,
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),
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],
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)
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def test_value_is_clear(value, expected_result):
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"""
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Test `is_clear` property of `Value` class.
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"""
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assert value.is_clear == expected_result
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@pytest.mark.parametrize(
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"value,expected_result",
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[
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pytest.param(
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EncryptedScalar(SignedInteger(5)),
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True,
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(3, 2)),
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False,
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),
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],
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)
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def test_value_is_scalar(value, expected_result):
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"""
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Test `is_scalar` property of `Value` class.
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"""
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assert value.is_scalar == expected_result
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@pytest.mark.parametrize(
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"value,expected_result",
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[
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pytest.param(
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EncryptedScalar(SignedInteger(5)),
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0,
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(3,)),
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1,
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(3, 2)),
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2,
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(5, 3, 2)),
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3,
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),
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],
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)
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def test_value_ndim(value, expected_result):
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"""
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Test `ndim` property of `Value` class.
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"""
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assert value.ndim == expected_result
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@pytest.mark.parametrize(
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"value,expected_result",
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[
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pytest.param(
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EncryptedScalar(SignedInteger(5)),
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1,
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(3,)),
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3,
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(3, 2)),
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6,
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(5, 3, 2)),
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30,
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(1,)),
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1,
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),
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pytest.param(
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EncryptedTensor(SignedInteger(5), shape=(1, 1)),
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1,
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),
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],
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)
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def test_value_size(value, expected_result):
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"""
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Test `size` property of `Value` class.
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"""
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assert value.size == expected_result
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