mirror of
https://github.com/luxfhe/torus.git
synced 2026-10-30 17:07:26 +00:00
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.
502 lines
17 KiB
Python
502 lines
17 KiB
Python
"""
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Tests of execution of dynamic assignment operation.
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"""
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import random
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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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"dtype,index,value_status,value",
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[
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pytest.param(
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fhe.tensor[fhe.int6, 5],
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(lambda _: np.random.randint(0, 5),),
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"clear",
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42,
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id="x[i] = 42 where x.shape = (5,) | 0 < i < 5",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5],
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(lambda _: np.random.randint(-5, 5),),
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"clear",
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42,
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id="x[i] = 42 where x.shape = (5,) | -5 < i < 5",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5],
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(lambda _: np.random.randint(0, 5),),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i] = y where x.shape = (5,) | 0 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5],
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(lambda _: np.random.randint(-5, 5),),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i] = y where x.shape = (5,) | -5 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 50],
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(lambda _: np.random.randint(0, 5),),
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"clear",
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42,
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id="x[i] = 42 where x.shape = (50,) | 0 < i < 5",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 50],
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(lambda _: np.random.randint(-5, 5),),
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"clear",
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42,
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id="x[i] = 42 where x.shape = (50,) | -5 < i < 5",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 50],
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(lambda _: np.random.randint(0, 5),),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i] = y where x.shape = (50,) | 0 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 50],
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(lambda _: np.random.randint(-5, 5),),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i] = y where x.shape = (50,) | -5 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5), 0),
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"clear",
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42,
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id="x[i, 0] = 42 where x.shape = (5, 3) | 0 < i < 5",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(-5, 5), 0),
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"clear",
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42,
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id="x[i, 0] = 42 where x.shape = (5, 3) | -5 < i < 5",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5), 0),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i, 0] = y where x.shape = (5, 3) | 0 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(-5, 5), 0),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i, 0] = y where x.shape = (5, 3) | -5 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 3, 5],
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(1, lambda _: np.random.randint(0, 5)),
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"clear",
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42,
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id="x[1, i] = 42 where x.shape = (5, 3) | 0 < i < 5",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 3, 5],
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(1, lambda _: np.random.randint(-5, 5)),
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"clear",
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42,
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id="x[1, i] = 42 where x.shape = (5, 3) | -5 < i < 5",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 3, 5],
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(1, lambda _: np.random.randint(0, 5)),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[1, i] = y where x.shape = (5, 3) | 0 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 3, 5],
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(1, lambda _: np.random.randint(-5, 5)),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[1, i] = y where x.shape = (5, 3) | -5 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5), lambda _: np.random.randint(0, 3)),
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"clear",
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42,
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id="x[i, j] = 42 where x.shape = (5, 3) | 0 < i < 5 | 0 < j < 3",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5), lambda _: np.random.randint(0, 3)),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i, j] = y where x.shape = (5, 3) | 0 < i < 5 | 0 < j < 3 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5), lambda _: np.random.randint(-3, 3)),
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"clear",
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42,
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id="x[i, j] = 42 where x.shape = (5, 3) | 0 < i < 5 | -3 < j < 3",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5), lambda _: np.random.randint(-3, 3)),
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i, j] = y where x.shape = (5, 3) | 0 < i < 5 | -3 < j < 3 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(-5, 5), lambda _: np.random.randint(0, 3)),
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"clear",
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42,
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id="x[i, j] = 42 where x.shape = (5, 3) | -5 < i < 5 | 0 < j < 3",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(-5, 5), lambda _: np.random.randint(0, 3)),
|
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i, j] = y where x.shape = (5, 3) | -5 < i < 5 | 0 < j < 3 | -10 < y < 10",
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),
|
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
|
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(lambda _: np.random.randint(-5, 5), lambda _: np.random.randint(-3, 3)),
|
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"clear",
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42,
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id="x[i, j] = 42 where x.shape = (5, 3) | -5 < i < 5 | -3 < j < 3",
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),
|
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
|
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(lambda _: np.random.randint(-5, 5), lambda _: np.random.randint(-3, 3)),
|
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i, j] = y where x.shape = (5, 3) | -5 < i < 5 | -3 < j < 3 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5),),
|
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"clear",
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42,
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id="x[i] = 42 where x.shape = (5, 3) | 0 < i < 5",
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),
|
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5),),
|
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i] = y where x.shape = (5, 3) | 0 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(-5, 5),),
|
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"clear",
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42,
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id="x[i] = 42 where x.shape = (5, 3) | -5 < i < 5",
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),
|
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(-5, 5),),
|
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"encrypted",
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lambda _: np.random.randint(-10, 10),
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id="x[i] = y where x.shape = (5, 3) | -5 < i < 5 | -10 < y < 10",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5),),
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"clear",
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[10, 20, 30],
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id="x[i] = [10, 20, 30] where x.shape = (5, 3) | 0 < i < 5",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(0, 5),),
|
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"encrypted",
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lambda _: np.random.randint(-10, 10, size=(3,)),
|
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id="x[i] = y where x.shape = (5, 3) | 0 < i < 5 | -10 < y < 10 | y.shape = (3,)",
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),
|
|
pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
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(lambda _: np.random.randint(-5, 5),),
|
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"clear",
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[10, 20, 30],
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id="x[i] = [10, 20, 30] where x.shape = (5, 3) | -5 < i < 5",
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),
|
|
pytest.param(
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fhe.tensor[fhe.int6, 5, 3],
|
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(lambda _: np.random.randint(-5, 5),),
|
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"encrypted",
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lambda _: np.random.randint(-10, 10, size=(3,)),
|
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id="x[i] = y where x.shape = (5, 3) | -5 < i < 5 | -10 < y < 10 | y.shape = (3,)",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 3, 5],
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(slice(None, None, None), lambda _: np.random.randint(0, 5)),
|
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"clear",
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42,
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id="x[:, i] = 42 where x.shape = (3, 5) | 0 < i < 5",
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),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 3, 5],
|
|
(slice(None, None, None), lambda _: np.random.randint(0, 5)),
|
|
"encrypted",
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|
lambda _: np.random.randint(-10, 10),
|
|
id="x[:, i] = y where x.shape = (3, 5) | 0 < i < 5 | -10 < y < 10",
|
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),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 3, 5],
|
|
(slice(None, None, None), lambda _: np.random.randint(-5, 5)),
|
|
"clear",
|
|
42,
|
|
id="x[:, i] = 42 where x.shape = (3, 5) | -5 < i < 5",
|
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),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 3, 5],
|
|
(slice(None, None, None), lambda _: np.random.randint(-5, 5)),
|
|
"encrypted",
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|
lambda _: np.random.randint(-10, 10),
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id="x[:, i] = y where x.shape = (3, 5) | -5 < i < 5 | -10 < y < 10",
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),
|
|
pytest.param(
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fhe.tensor[fhe.int6, 3, 5],
|
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(slice(None, None, None), lambda _: np.random.randint(0, 5)),
|
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"clear",
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[10, 20, 30],
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id="x[:, i] = [10, 20, 30] where x.shape = (3, 5) | 0 < i < 5",
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),
|
|
pytest.param(
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fhe.tensor[fhe.int6, 3, 5],
|
|
(slice(None, None, None), lambda _: np.random.randint(0, 5)),
|
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"encrypted",
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|
lambda _: np.random.randint(-10, 10, size=(3,)),
|
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id="x[:, i] = y where x.shape = (3, 5) | 0 < i < 5 | -10 < y < 10 | y.shape = (3,)",
|
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),
|
|
pytest.param(
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fhe.tensor[fhe.int6, 3, 5],
|
|
(slice(None, None, None), lambda _: np.random.randint(-5, 5)),
|
|
"clear",
|
|
[10, 20, 30],
|
|
id="x[:, i] = [10, 20, 30] where x.shape = (3, 5) | -5 < i < 5",
|
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),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 3, 5],
|
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(slice(None, None, None), lambda _: np.random.randint(-5, 5)),
|
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"encrypted",
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|
lambda _: np.random.randint(-10, 10, size=(3,)),
|
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id="x[:, i] = y where x.shape = (3, 5) | -5 < i < 5 | -10 < y < 10 | y.shape = (3,)",
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),
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pytest.param(
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fhe.tensor[fhe.int6, 10, 9, 8],
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(slice(1, 3, None), lambda _: np.random.randint(0, 9), slice(4, 6, None)),
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"clear",
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42,
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id="x[1:3, i, 4:6] = 42 where x.shape = (10, 9, 8) | 0 < i < 9",
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),
|
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pytest.param(
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fhe.tensor[fhe.int6, 10, 9, 8],
|
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(slice(1, 3, None), lambda _: np.random.randint(-9, 9), slice(4, 6, None)),
|
|
"clear",
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42,
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id="x[1:3, i, 4:6] = 42 where x.shape = (10, 9, 8) | -9 < i < 9",
|
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),
|
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pytest.param(
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fhe.tensor[fhe.int6, 10, 9, 8],
|
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(
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lambda _: np.random.randint(0, 10),
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slice(2, 5, None),
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lambda _: np.random.randint(0, 8),
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),
|
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"clear",
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42,
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id="x[i, 2:5, j] = 42 where x.shape = (10, 9, 8) | 0 < i < 10 | 0 < j < 8",
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),
|
|
pytest.param(
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|
fhe.tensor[fhe.int6, 5],
|
|
(lambda _: np.random.randint(0, 5, size=(3,)),),
|
|
"clear",
|
|
42,
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id="x[i] = 42 where x.shape = (5,) | 0 < i < 5 | i.shape = (3,)",
|
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),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 5],
|
|
(lambda _: np.random.randint(-5, 5, size=(3,)),),
|
|
"clear",
|
|
42,
|
|
id="x[i] = 42 where x.shape = (5,) | -5 < i < 5 | i.shape = (3,)",
|
|
),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 5],
|
|
(lambda _: np.random.randint(0, 5, size=(3,)),),
|
|
"encrypted",
|
|
lambda _: np.random.randint(-10, 10),
|
|
id="x[i] = y where x.shape = (5,) | 0 < i < 5 | i.shape = (3,) | -10 < y < 10",
|
|
),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 5],
|
|
(lambda _: np.random.randint(-5, 5, size=(3,)),),
|
|
"encrypted",
|
|
lambda _: np.random.randint(-10, 10),
|
|
id="x[i] = y where x.shape = (5,) | -5 < i < 5 | i.shape = (3,) | -10 < y < 10",
|
|
),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 5],
|
|
(lambda _: np.random.randint(0, 5, size=(3,)),),
|
|
"clear",
|
|
[10, 20, 30],
|
|
id="x[i] = [10, 20, 30] where x.shape = (5,) | 0 < i < 5 | i.shape = (3,)",
|
|
),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 5],
|
|
(lambda _: np.random.randint(-5, 5, size=(3,)),),
|
|
"clear",
|
|
[10, 20, 30],
|
|
id="x[i] = [10, 20, 30] where x.shape = (5,) | -5 < i < 5 | i.shape = (3,)",
|
|
),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 5],
|
|
(lambda _: np.random.randint(0, 5, size=(3,)),),
|
|
"encrypted",
|
|
lambda _: np.random.randint(-10, 10, size=(3,)),
|
|
id=(
|
|
"x[i] = y where x.shape = (5,) "
|
|
"| 0 < i < 5 | i.shape = (3,) "
|
|
"| -10 < y < 10 | y.shape = (3,)"
|
|
),
|
|
),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 5],
|
|
(lambda _: np.random.randint(-5, 5, size=(3,)),),
|
|
"encrypted",
|
|
lambda _: np.random.randint(-10, 10, size=(3,)),
|
|
id=(
|
|
"x[i] = y where x.shape = (5,) "
|
|
"| -5 < i < 5 | i.shape = (3,) "
|
|
"| -10 < y < 10 | y.shape = (3,)"
|
|
),
|
|
),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 20],
|
|
(lambda _: np.random.randint(0, 20, size=(3, 2)),),
|
|
"clear",
|
|
[[10, 11], [20, 21], [30, 31]],
|
|
id=(
|
|
"x[i] = [[10, 11], [20, 21], [30, 31]] where x.shape = (20,) "
|
|
"| 0 < i < 20 | i.shape = (3, 2)"
|
|
),
|
|
),
|
|
pytest.param(
|
|
fhe.tensor[fhe.int6, 20],
|
|
(lambda _: np.random.randint(0, 20, size=(3, 2)),),
|
|
"clear",
|
|
[42, 24],
|
|
id="x[i] = [42, 24] where x.shape = (20,) | 0 < i < 20 | i.shape = (3, 2)",
|
|
),
|
|
],
|
|
)
|
|
def test_dynamic_assignment(dtype, index, value_status, value, helpers):
|
|
"""
|
|
Test dynamic assignment.
|
|
"""
|
|
|
|
dynamic_index_positions = []
|
|
dynamic_indices = []
|
|
|
|
for position, indexing_element in enumerate(index):
|
|
if callable(indexing_element):
|
|
dynamic_indices.append(indexing_element)
|
|
dynamic_index_positions.append(position)
|
|
|
|
processed_index = list(index)
|
|
|
|
def f(tensor, *args, value):
|
|
for cursor, position in enumerate(dynamic_index_positions):
|
|
processed_index[position] = args[cursor]
|
|
|
|
tensor[tuple(processed_index)] = value
|
|
return tensor
|
|
|
|
# pylint: disable=invalid-name
|
|
|
|
if len(dynamic_index_positions) == 1:
|
|
if callable(value):
|
|
|
|
def function(tensor, i0, value=value):
|
|
return f(tensor, i0, value=value)
|
|
|
|
else:
|
|
|
|
def function(tensor, i0):
|
|
return f(tensor, i0, value=value)
|
|
|
|
elif len(dynamic_index_positions) == 2:
|
|
if callable(value):
|
|
|
|
def function(tensor, i0, i1, value=value):
|
|
return f(tensor, i0, i1, value=value)
|
|
|
|
else:
|
|
|
|
def function(tensor, i0, i1):
|
|
return f(tensor, i0, i1, value=value)
|
|
|
|
elif len(dynamic_index_positions) == 3:
|
|
if callable(value):
|
|
|
|
def function(tensor, i0, i1, i2, value=value):
|
|
return f(tensor, i0, i1, i2, value=value)
|
|
|
|
else:
|
|
|
|
def function(tensor, i0, i1, i2):
|
|
return f(tensor, i0, i1, i2, value=value)
|
|
|
|
elif len(dynamic_index_positions) == 4:
|
|
if callable(value):
|
|
|
|
def function(tensor, i0, i1, i2, i3, value=value):
|
|
return f(tensor, i0, i1, i2, i3, value=value)
|
|
|
|
else:
|
|
|
|
def function(tensor, i0, i1, i2, i3):
|
|
return f(tensor, i0, i1, i2, i3, value=value)
|
|
|
|
else:
|
|
message = (
|
|
f"expected at least 1 at most 4 dynamic indexing elements "
|
|
f"but got {len(dynamic_index_positions)}"
|
|
)
|
|
raise RuntimeError(message)
|
|
|
|
# pylint: enable=invalid-name
|
|
|
|
encryption_status = {"tensor": "encrypted"}
|
|
inputset_types = [dtype]
|
|
|
|
cursor = 0
|
|
for indexing_element in index:
|
|
if callable(indexing_element):
|
|
encryption_status[f"i{cursor}"] = "clear"
|
|
inputset_types.append(indexing_element)
|
|
cursor += 1
|
|
if callable(value):
|
|
encryption_status["value"] = value_status
|
|
inputset_types.append(value)
|
|
|
|
configuration = helpers.configuration()
|
|
compiler = fhe.Compiler(function, encryption_status)
|
|
|
|
inputset = fhe.inputset(*inputset_types)
|
|
circuit = compiler.compile(inputset, configuration, show_mlir=True)
|
|
|
|
for sample in random.sample(inputset, 8):
|
|
helpers.check_execution(circuit, function, list(sample))
|