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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.
169 lines
4.6 KiB
Python
169 lines
4.6 KiB
Python
"""
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Benchmarks of the levenshtein distance example.
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"""
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from functools import lru_cache
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from pathlib import Path
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import py_progress_tracker as progress
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from torus import fhe
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from examples.levenshtein_distance.levenshtein_distance import Alphabet, LevenshteinDistance
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@lru_cache
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def levenshtein_on_server(
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server: fhe.Server,
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x: tuple[fhe.Value],
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y: tuple[fhe.Value],
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evaluation_keys: fhe.EvaluationKeys,
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):
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"""
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Compute levenshtein distance on a server.
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"""
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if len(x) == 0:
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return server.run(
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len(y),
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function_name="constant",
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evaluation_keys=evaluation_keys,
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)
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if len(y) == 0:
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return server.run(
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len(x),
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function_name="constant",
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evaluation_keys=evaluation_keys,
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)
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if_equal = levenshtein_on_server(server, x[1:], y[1:], evaluation_keys)
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case_1 = levenshtein_on_server(server, x[1:], y, evaluation_keys)
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case_2 = levenshtein_on_server(server, x, y[1:], evaluation_keys)
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case_3 = if_equal
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is_equal = server.run(
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x[0],
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y[0],
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function_name="equal",
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evaluation_keys=evaluation_keys,
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)
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result = server.run(
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is_equal,
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if_equal,
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case_1,
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case_2,
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case_3,
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function_name="mix",
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evaluation_keys=evaluation_keys,
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)
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return result
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def targets():
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"""
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Generates targets to benchmark.
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"""
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result = []
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for alphabet in ["ACTG", "string"]:
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for max_string_length in [2, 4, 8]:
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result.append(
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{
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"id": (
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f"levenshtein-distance :: "
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f"Levenshtein distance "
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f"| alphabet = {alphabet} "
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f"| max_string_size = {max_string_length}"
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),
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"name": (
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f"Levenshtein distance between two strings "
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f"of length {max_string_length} "
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f"from {alphabet} alphabet"
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),
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"parameters": {
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"alphabet": alphabet,
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"max_string_length": max_string_length,
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},
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}
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)
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return result
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@progress.track(targets())
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def main(alphabet, max_string_length):
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"""
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Benchmark a target.
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Args:
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alphabet:
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alphabet of the inputs
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max_string_length:
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maximum size of the inputs
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"""
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cached_server = Path(f"levenshtein.{alphabet}.{max_string_length}.server.zip")
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alphabet = Alphabet.init_by_name(alphabet)
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print("Compiling...")
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if cached_server.exists():
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server = fhe.Server.load(cached_server)
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client = fhe.Client(server.client_specs, keyset_cache_directory=".keys")
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else:
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levenshtein_distance = LevenshteinDistance(
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alphabet,
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max_string_length,
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configuration=fhe.Configuration(
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enable_unsafe_features=True,
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use_insecure_key_cache=True,
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insecure_key_cache_location=".keys",
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),
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)
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levenshtein_distance.module.server.save(cached_server)
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server = levenshtein_distance.module.server
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client = levenshtein_distance.module.client
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print("Generating keys...")
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client.keygen()
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print("Warming up...")
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sample_a = alphabet.random_string(max_string_length)
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sample_b = alphabet.random_string(max_string_length)
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encrypted_sample_a = tuple(
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client.encrypt(ai, None, function_name="equal")[0] for ai in alphabet.encode(sample_a)
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)
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encrypted_sample_b = tuple(
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client.encrypt(None, bi, function_name="equal")[1] for bi in alphabet.encode(sample_b)
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)
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levenshtein_on_server(
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server,
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encrypted_sample_a,
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encrypted_sample_b,
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client.evaluation_keys,
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)
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for i in range(5):
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print(f"Running subsample {i + 1} out of 5...")
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sample_a = alphabet.random_string(max_string_length)
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sample_b = alphabet.random_string(max_string_length)
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encrypted_sample_a = tuple(
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client.encrypt(ai, None, function_name="equal")[0] for ai in alphabet.encode(sample_a)
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)
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encrypted_sample_b = tuple(
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client.encrypt(None, bi, function_name="equal")[1] for bi in alphabet.encode(sample_b)
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)
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with progress.measure(id="evaluation-time-ms", label="Evaluation Time (ms)"):
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levenshtein_on_server(
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server,
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encrypted_sample_a,
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encrypted_sample_b,
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client.evaluation_keys,
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)
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