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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.
183 lines
5.3 KiB
Python
183 lines
5.3 KiB
Python
"""
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Tests of execution of comparison operations.
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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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from torus.fhe.dtypes import Integer
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from torus.fhe.values import ValueDescription
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cases = [
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# uint1 (.) int1 triggers a special case in subtraction trick
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[
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# operation
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("==", lambda x, y: x == y),
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# bit widths
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1,
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1,
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# signednesses
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False,
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True,
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# shapes
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(),
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(),
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# strategy
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fhe.ComparisonStrategy.THREE_TLU_CASTED,
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]
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]
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for lhs_bit_width in range(1, 6):
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for rhs_bit_width in range(1, 6):
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for lhs_is_signed in [False, True]:
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for rhs_is_signed in [False, True]:
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cases += [
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[
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# operation
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operation,
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# bit widths
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lhs_bit_width,
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rhs_bit_width,
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# signednesses
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lhs_is_signed,
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rhs_is_signed,
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# shapes
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(),
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(),
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# strategy
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None,
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]
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for operation in [("==", lambda x, y: x == y), ("<", lambda x, y: x < y)]
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]
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for _ in range(35):
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cases.append(
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[
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# operation
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random.choice(
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[
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("==", lambda x, y: x == y),
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("!=", lambda x, y: x != y),
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("<", lambda x, y: x < y),
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("<=", lambda x, y: x <= y),
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(">", lambda x, y: x > y),
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(">=", lambda x, y: x >= y),
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]
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),
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# bit widths
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random.choice([1, 2, 3, 4, 5, 6, 8, 14, 15, 16]),
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random.choice([1, 2, 3, 4, 5, 6, 8, 14, 15, 16]),
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# signednesses
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random.choice([False, True]),
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random.choice([False, True]),
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# shapes
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random.choice([(), (2,), (3, 2)]),
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random.choice([(), (2,), (3, 2)]),
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# strategy
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random.choice(
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[
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fhe.ComparisonStrategy.ONE_TLU_PROMOTED,
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fhe.ComparisonStrategy.THREE_TLU_CASTED,
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fhe.ComparisonStrategy.TWO_TLU_BIGGER_PROMOTED_SMALLER_CASTED,
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fhe.ComparisonStrategy.TWO_TLU_BIGGER_CASTED_SMALLER_PROMOTED,
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fhe.ComparisonStrategy.THREE_TLU_BIGGER_CLIPPED_SMALLER_CASTED,
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fhe.ComparisonStrategy.TWO_TLU_BIGGER_CLIPPED_SMALLER_PROMOTED,
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fhe.ComparisonStrategy.CHUNKED,
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]
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),
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]
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)
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# pylint: disable=redefined-outer-name
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@pytest.mark.parametrize(
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"operation,"
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"lhs_bit_width,rhs_bit_width,"
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"lhs_is_signed,rhs_is_signed,"
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"lhs_shape,rhs_shape,"
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"strategy",
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cases,
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)
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def test_comparison(
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operation,
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lhs_bit_width,
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rhs_bit_width,
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lhs_is_signed,
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rhs_is_signed,
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lhs_shape,
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rhs_shape,
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strategy,
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helpers,
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):
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"""
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Test comparison operations between encrypted integers.
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"""
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name, function = operation
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lhs_dtype = Integer(is_signed=lhs_is_signed, bit_width=lhs_bit_width)
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rhs_dtype = Integer(is_signed=rhs_is_signed, bit_width=rhs_bit_width)
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lhs_description = ValueDescription(lhs_dtype, shape=lhs_shape, is_encrypted=True)
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rhs_description = ValueDescription(rhs_dtype, shape=rhs_shape, is_encrypted=True)
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print()
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print()
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print(
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f"[{lhs_description}] ({name}) [{rhs_description}]"
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+ (f" {{{strategy}}}" if strategy is not None else "")
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)
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print()
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print()
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parameter_encryption_statuses = {"x": "encrypted", "y": "encrypted"}
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configuration = helpers.configuration().fork(use_insecure_key_cache=False)
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if strategy is not None:
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configuration = configuration.fork(comparison_strategy_preference=[strategy])
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compiler = fhe.Compiler(function, parameter_encryption_statuses)
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inputset = [
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(
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np.random.randint(lhs_dtype.min(), lhs_dtype.max() + 1, size=lhs_shape),
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np.random.randint(rhs_dtype.min(), rhs_dtype.max() + 1, size=rhs_shape),
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)
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for _ in range(100)
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]
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circuit = compiler.compile(inputset, configuration)
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samples = [
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[
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np.zeros(lhs_shape, dtype=np.int64),
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np.zeros(rhs_shape, dtype=np.int64),
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],
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[
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np.ones(lhs_shape, dtype=np.int64) * lhs_dtype.min(),
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np.ones(rhs_shape, dtype=np.int64) * rhs_dtype.min(),
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],
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[
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np.ones(lhs_shape, dtype=np.int64) * lhs_dtype.max(),
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np.ones(rhs_shape, dtype=np.int64) * rhs_dtype.min(),
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],
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[
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np.ones(lhs_shape, dtype=np.int64) * lhs_dtype.max(),
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np.ones(rhs_shape, dtype=np.int64) * rhs_dtype.max(),
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],
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[
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np.random.randint(lhs_dtype.min(), lhs_dtype.max() + 1, size=lhs_shape),
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np.random.randint(rhs_dtype.min(), rhs_dtype.max() + 1, size=rhs_shape),
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],
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]
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for sample in samples:
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helpers.check_execution(
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circuit,
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function,
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sample,
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retries=5,
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only_simulation=(max(lhs_bit_width, rhs_bit_width) > 7),
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)
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