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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.
273 lines
7.0 KiB
Python
273 lines
7.0 KiB
Python
"""
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Tests of execution of static indexing 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,function",
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[
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pytest.param(
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(3,),
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lambda x: x[0],
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id="x[0] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[1],
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id="x[1] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[2],
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id="x[2] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[-1],
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id="x[-1] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[-2],
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id="x[-2] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[-3],
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id="x[-3] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[0:1],
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id="x[0:1] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[1:2],
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id="x[1:2] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[2:3],
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id="x[2:3] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[0:2],
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id="x[0:2] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[1:3],
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id="x[1:3] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[0:3],
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id="x[0:3] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[2:0:-1],
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id="x[2:0:-1] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[::-1],
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id="x[::-1] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[:-1],
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id="x[:-1] where x.shape == (3,)",
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),
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pytest.param(
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(3,),
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lambda x: x[-2:],
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id="x[-2:] where x.shape == (3,)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[0, 0],
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id="x[0, 0] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[0, -1],
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id="x[0, -1] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[-1, 0],
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id="x[-1, 0] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[-1, -1],
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id="x[-1, -1] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[2, 1],
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id="x[2, 1] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[0],
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id="x[0] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[:, 0],
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id="x[:, 0] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[-1],
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id="x[-1] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[:, -1],
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id="x[:, -1] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[1:3, 1:3],
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id="x[1:3, 1:3] where x.shape == (3, 4)",
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),
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pytest.param(
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(3, 4),
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lambda x: x[::-1],
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id="x[::-1] where x.shape == (3, 4)",
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),
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pytest.param(
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(10,),
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lambda x: x[slice(np.int64(8), np.int64(2), np.int64(-2))],
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id="x[8:2:-2] where x.shape == (10,)",
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),
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pytest.param(
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(5,),
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lambda x: x[[3, 1, 2]],
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id="x[[3, 1, 2]] where x.shape == (5,)",
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),
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pytest.param(
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(5,),
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lambda x: x[
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[
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[3, 0],
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[1, 2],
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]
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],
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id="x[[[3, 0], [1, 2]]] where x.shape == (5,)",
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),
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pytest.param(
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(5, 4),
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lambda x: x[
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[0, 0, 4, 4],
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[0, 3, 0, 3],
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],
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id="x[[0, 0, 4, 4], [0, 3, 0, 3]] where x.shape == (5, 4)",
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),
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pytest.param(
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(5, 4),
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lambda x: x[
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0,
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[0, 3, 0, 3],
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],
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id="x[0, [0, 3, 0, 3]] where x.shape == (5, 4)",
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),
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pytest.param(
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(5, 4),
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lambda x: x[
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[0, 0, 4, 4],
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0,
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],
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id="x[[0, 0, 4, 4], 0] where x.shape == (5, 4)",
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),
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pytest.param(
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(5, 4),
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lambda x: x[
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[[0, 0], [4, 4]],
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[[0, 3], [0, 3]],
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],
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id="x[[[0, 0], [4, 4]], [[0, 3], [0, 3]]] where x.shape == (5, 4)",
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),
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pytest.param(
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(5, 4),
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lambda x: x[0, [[0, 3], [0, 3]]],
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id="x[0, [[0, 3], [0, 3]]] where x.shape == (5, 4)",
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),
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pytest.param(
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(5, 4),
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lambda x: x[[[0, 3], [0, 3]], 0],
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id="x[[[0, 3], [0, 3]], 0] where x.shape == (5, 4)",
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),
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],
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)
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def test_static_indexing(shape, function, helpers):
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"""
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Test static indexing.
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"""
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configuration = helpers.configuration()
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compiler = fhe.Compiler(function, {"x": "encrypted"})
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inputset = [np.random.randint(0, 2**5, size=shape) for _ in range(100)]
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circuit = compiler.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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def test_bad_static_indexing(helpers):
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"""
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Test static indexing with bad parameters.
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"""
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configuration = helpers.configuration()
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# with float
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# ----------
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compiler = fhe.Compiler(lambda x: x[1.5], {"x": "encrypted"})
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inputset = [[5, 2, 4]]
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with pytest.raises(ValueError) as excinfo:
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compiler.compile(inputset, configuration)
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assert str(excinfo.value) == (
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"Tracer<output=EncryptedTensor<uint3, shape=(3,)>> cannot be indexed with 1.5"
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)
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# with bad slice
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# --------------
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compiler = fhe.Compiler(lambda x: x[slice(1.5, 2.5, None)], {"x": "encrypted"})
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inputset = [[5, 2, 4]]
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with pytest.raises(ValueError) as excinfo:
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compiler.compile(inputset, configuration)
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assert str(excinfo.value) == (
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"Tracer<output=EncryptedTensor<uint3, shape=(3,)>> cannot be indexed with 1.5:2.5"
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)
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# with multiple values
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# --------------------
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compiler = fhe.Compiler(lambda x: x[0, 1.5], {"x": "encrypted"})
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inputset = [[5, 2, 4]]
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with pytest.raises(ValueError) as excinfo:
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compiler.compile(inputset, configuration)
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assert str(excinfo.value) == (
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"Tracer<output=EncryptedTensor<uint3, shape=(3,)>> cannot be indexed with 0, 1.5"
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)
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