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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.
69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
"""
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Tests of 'multivariate' extension.
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"""
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import pytest
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from torus import fhe
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def test_bad_multivariate(helpers):
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"""
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Test 'multivariate' extension with bad parameters.
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"""
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# direct definition without outputs
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with pytest.raises(ValueError) as excinfo:
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@fhe.circuit({"x": "encrypted"}, helpers.configuration())
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def no_outputs_in_direct_definition(x: fhe.uint3):
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return fhe.multivariate(lambda x: x**2)(x)
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assert str(excinfo.value) == (
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"Multivariate extension requires `outputs` argument for direct circuit definition "
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"(e.g., fhe.multivariate(function, outputs=fhe.uint4)(x, y, z))"
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)
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# clear inputs
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with pytest.raises(ValueError) as excinfo:
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@fhe.compiler({"x": "encrypted", "y": "clear"})
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def clear_inputs(x, y):
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return fhe.multivariate(lambda x, y: x * y)(x, y)
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inputset = [(1, 50)]
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clear_inputs.compile(inputset, helpers.configuration())
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assert str(excinfo.value) == (
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"Multivariate extension requires all of its inputs to be encrypted"
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)
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# rounded inputs
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with pytest.raises(ValueError) as excinfo:
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@fhe.compiler({"x": "encrypted", "y": "encrypted"})
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def rounded_inputs(x, y):
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y = fhe.round_bit_pattern(y, lsbs_to_remove=2)
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return fhe.multivariate(lambda x, y: x * y)(x, y)
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inputset = [(1, 50)]
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rounded_inputs.compile(inputset, helpers.configuration())
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assert str(excinfo.value) == "Multivariate extension cannot be used with rounded inputs"
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# bad output shape
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with pytest.raises(ValueError) as excinfo:
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@fhe.compiler({"x": "encrypted", "y": "encrypted"})
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def invalid_output_shape(x, y):
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return fhe.multivariate(lambda x, y: (x * y)[0])(x, y)
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inputset = [(1, [1, 2, 3])]
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invalid_output_shape.compile(inputset, helpers.configuration())
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assert str(excinfo.value) == "Function <lambda> is not compatible with fhe.multivariate"
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