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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.
181 lines
6.1 KiB
Python
181 lines
6.1 KiB
Python
"""
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Tests of `Tracer` 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 UnsignedInteger
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from torus.fhe.tracing import Tracer
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from torus.fhe.tracing.typing import uint4
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from torus.fhe.values import EncryptedTensor
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def bad_assignment(x):
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"""
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Function with an invalid assignment.
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"""
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idx = [[1, 2], [1, 2, 3], [1]]
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x[idx] = 0
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return x
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@pytest.mark.parametrize(
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"function,parameters,expected_error,expected_message",
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[
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pytest.param(
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lambda x: np.ravel(x),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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RuntimeError,
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"Function 'np.ravel' is not supported",
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),
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pytest.param(
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lambda x: np.sum(x, initial=42),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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RuntimeError,
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"Function 'np.sum' is not supported with kwarg 'initial'",
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),
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pytest.param(
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lambda x: np.absolute(x, where=False),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(1, 2, 3))},
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RuntimeError,
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"Function 'np.absolute' is not supported with kwarg 'where'",
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),
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pytest.param(
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lambda x: np.multiply.outer(x, [1, 2, 3]),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(4,))},
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RuntimeError,
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"Only __call__ hook is supported for numpy ufuncs",
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),
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pytest.param(
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lambda x: x.astype(uint4),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(4,))},
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ValueError,
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"`astype` method must be called with a numpy type "
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"for compilation (e.g., value.astype(np.int64))",
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),
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pytest.param(
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lambda x: x + 1 if x else x + x,
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{"x": EncryptedTensor(UnsignedInteger(7), shape=())},
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RuntimeError,
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"Branching within circuits is not possible",
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),
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pytest.param(
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lambda x: x[["abc", 3, 2.2, (1,)]],
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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ValueError,
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"Tracer<output=EncryptedTensor<uint7, shape=(3, 2)>> "
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"cannot be indexed with ['abc', 3, 2.2, (1,)]",
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),
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pytest.param(
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bad_assignment,
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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ValueError,
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"Tracer<output=EncryptedTensor<uint7, shape=(3, 2)>>[[[1, 2], [1, 2, 3], [1]]] "
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"cannot be assigned 0",
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),
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],
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)
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def test_tracer_bad_trace(function, parameters, expected_error, expected_message):
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"""
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Test `trace` function of `Tracer` class with bad parameters.
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"""
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with pytest.raises(expected_error) as excinfo:
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Tracer.trace(function, parameters)
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assert str(excinfo.value) == expected_message
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@pytest.mark.parametrize(
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"function,parameters,expected_message",
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[
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pytest.param(
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lambda x: x.astype(np.int8),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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(
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"Warning: When using `value.astype(newtype)` "
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"with an integer newtype, "
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"only use `np.int64` as the newtype "
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"to avoid unexpected overflows "
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"during inputset evaluation"
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),
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),
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pytest.param(
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lambda x: x.astype(np.int16),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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(
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"Warning: When using `value.astype(newtype)` "
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"with an integer newtype, "
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"only use `np.int64` as the newtype "
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"to avoid unexpected overflows "
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"during inputset evaluation"
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),
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),
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pytest.param(
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lambda x: x.astype(np.int32),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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(
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"Warning: When using `value.astype(newtype)` "
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"with an integer newtype, "
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"only use `np.int64` as the newtype "
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"to avoid unexpected overflows "
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"during inputset evaluation"
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),
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),
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pytest.param(
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lambda x: x.astype(np.uint8),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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(
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"Warning: When using `value.astype(newtype)` "
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"with an integer newtype, "
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"only use `np.int64` as the newtype "
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"to avoid unexpected overflows "
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"during inputset evaluation"
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),
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),
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pytest.param(
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lambda x: x.astype(np.uint16),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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(
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"Warning: When using `value.astype(newtype)` "
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"with an integer newtype, "
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"only use `np.int64` as the newtype "
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"to avoid unexpected overflows "
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"during inputset evaluation"
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),
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),
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pytest.param(
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lambda x: x.astype(np.uint32),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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(
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"Warning: When using `value.astype(newtype)` "
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"with an integer newtype, "
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"only use `np.int64` as the newtype "
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"to avoid unexpected overflows "
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"during inputset evaluation"
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),
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),
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pytest.param(
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lambda x: x.astype(np.uint64),
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{"x": EncryptedTensor(UnsignedInteger(7), shape=(3, 2))},
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(
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"Warning: When using `value.astype(newtype)` "
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"with an integer newtype, "
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"only use `np.int64` as the newtype "
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"to avoid unexpected overflows "
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"during inputset evaluation"
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),
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),
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],
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)
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def test_tracer_warning_trace(function, parameters, expected_message, capsys):
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"""
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Test `trace` function of `Tracer` class with parameters that result in a warning.
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"""
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Tracer.trace(function, parameters)
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captured = capsys.readouterr()
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assert captured.out.strip() == expected_message
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