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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.
177 lines
3.6 KiB
Python
177 lines
3.6 KiB
Python
"""
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Tests of execution of reshape 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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"shape,newshape",
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[
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pytest.param(
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(12,),
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(12, 1),
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),
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pytest.param(
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(12,),
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(1, 12),
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),
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pytest.param(
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(12,),
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(3, 4),
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),
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pytest.param(
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(12,),
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(3, 2, 2),
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),
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pytest.param(
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(3, 4),
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12,
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),
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pytest.param(
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(3, 4),
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(12,),
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),
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pytest.param(
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(3, 4),
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(4, 3),
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),
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pytest.param(
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(3, 4),
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(2, 2, 3),
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),
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pytest.param(
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(3, 4),
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(2, 3, 2),
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),
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pytest.param(
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(3, 4),
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(3, 2, 2),
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),
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pytest.param(
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(3, 4),
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(3, 1, 4),
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),
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pytest.param(
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(3, 4),
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(12, 1),
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),
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pytest.param(
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(3, 4),
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(-1,),
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),
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pytest.param(
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(3, 4),
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-1,
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),
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pytest.param(
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(2, 2, 3),
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(3, 4),
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),
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pytest.param(
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(2, 2, 3),
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(4, 3),
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),
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pytest.param(
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(2, 2, 3),
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(3, 2, 2),
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),
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pytest.param(
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(2, 3, 4, 5, 6),
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(6, 4, 30),
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),
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pytest.param(
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(6, 4, 30),
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(2, 3, 4, 5, 6),
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),
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pytest.param(
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(2, 3, 4, 5, 6),
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(2, 60, 6),
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),
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pytest.param(
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(2, 60, 6),
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(2, 3, 4, 5, 6),
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),
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pytest.param(
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(2, 3, 2, 3, 4),
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(6, 6, -1),
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),
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pytest.param(
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(2, 3, 2, 3, 4),
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(6, -1, 12),
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),
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pytest.param(
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(2, 3, 2, 3, 4),
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(-1, 18, 4),
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),
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],
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)
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def test_reshape(shape, newshape, helpers):
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"""
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Test reshape.
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"""
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configuration = helpers.configuration()
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@fhe.compiler({"x": "encrypted"})
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def function(x):
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return np.reshape(x, newshape)
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@fhe.compiler({"x": "encrypted"})
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def method(x):
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return x.reshape(newshape)
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@fhe.compiler({"x": "encrypted"})
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def args(x):
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return x.reshape(*(newshape if isinstance(newshape, tuple) else (newshape,)))
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inputset = [np.random.randint(0, 2**5, size=shape) for i in range(100)]
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function_circuit = function.compile(inputset, configuration)
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method_circuit = method.compile(inputset, configuration)
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args_circuit = args.compile(inputset, configuration)
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sample = np.random.randint(0, 2**5, size=shape)
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helpers.check_execution(function_circuit, function, sample)
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helpers.check_execution(method_circuit, method, sample)
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helpers.check_execution(args_circuit, args, sample)
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@pytest.mark.parametrize(
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"shape",
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[
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pytest.param(
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(12,),
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),
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pytest.param(
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(3, 4),
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),
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pytest.param(
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(2, 2, 3),
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),
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pytest.param(
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(2, 3, 4, 5, 6),
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),
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],
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)
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def test_flatten(shape, helpers):
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"""
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Test flatten.
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"""
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configuration = helpers.configuration()
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@fhe.compiler({"x": "encrypted"})
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def function(x):
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return x.flatten()
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inputset = [np.random.randint(0, 2**5, size=shape) for i in range(100)]
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circuit = function.compile(inputset, configuration)
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sample = np.random.randint(0, 2**5, size=shape)
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helpers.check_execution(circuit, function, sample)
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