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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.
135 lines
3.9 KiB
Python
135 lines
3.9 KiB
Python
"""
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Benchmark primitive operations.
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"""
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# pylint: disable=import-error,cell-var-from-loop,redefined-outer-name
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import random
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import py_progress_tracker as progress
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from torus import fhe
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targets = []
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configuration = fhe.Configuration()
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# Table Lookup
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for bit_width in range(2, 8 + 1):
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targets.append(
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{
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"id": f"table-lookup :: tlu[eint{bit_width}]",
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"name": f"{bit_width}-bit table lookup",
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"parameters": {
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"function": lambda x: x // 2,
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"encryption": {"x": "encrypted"},
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"inputset": fhe.inputset(lambda _: random.randint(0, (2**bit_width) - 1)),
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"configuration": configuration,
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},
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}
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)
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# Encrypted Multiplication
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for bit_width in range(2, 8 + 1):
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targets.append(
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{
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"id": f"encrypted-multiplication :: eint{bit_width} * eint{bit_width}",
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"name": f"{bit_width}-bit encrypted multiplication",
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"parameters": {
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"function": lambda x, y: x * y,
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"encryption": {"x": "encrypted", "y": "encrypted"},
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"inputset": fhe.inputset(
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lambda _: random.randint(0, (2**bit_width) - 1),
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lambda _: random.randint(0, (2**bit_width) - 1),
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),
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"configuration": configuration,
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},
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}
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)
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@progress.track(targets)
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def main(function, encryption, inputset, configuration):
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"""
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Benchmark a target.
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Args:
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function:
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function to benchmark
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encryption:
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encryption status of the arguments of the function
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inputset:
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inputset to use for compiling the function
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configuration:
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configuration to use for compilation
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"""
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compiler = fhe.Compiler(function, encryption)
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print("Compiling...")
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with progress.measure(id="compilation-time-ms", label="Compilation Time (ms)"):
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circuit = compiler.compile(inputset, configuration)
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progress.measure(
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id="complexity",
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label="Complexity",
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value=circuit.complexity,
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)
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print("Generating keys...")
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with progress.measure(id="key-generation-time-ms", label="Key Generation Time (ms)"):
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circuit.keygen(force=True)
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progress.measure(
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id="evaluation-key-size-mb",
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label="Evaluation Key Size (MB)",
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value=(len(circuit.keys.evaluation.serialize()) / (1024 * 1024)),
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)
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# pylint: disable=unused-variable
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print("Warming up...")
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sample = random.choice(inputset)
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encrypted = circuit.encrypt(*sample)
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ran = circuit.run(encrypted)
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decrypted = circuit.decrypt(ran) # noqa: F841
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# pylint: enable=unused-variable
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def calculate_input_output_size(input_output):
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if isinstance(input_output, tuple):
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result = sum(len(value.serialize()) for value in input_output)
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else:
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result = len(input_output.serialize())
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return result / (1024 * 1024)
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progress.measure(
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id="input-ciphertext-size-mb",
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label="Input Ciphertext Size (MB)",
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value=calculate_input_output_size(encrypted),
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)
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progress.measure(
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id="output-ciphertext-size-mb",
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label="Output Ciphertext Size (MB)",
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value=calculate_input_output_size(ran),
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)
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for i in range(10):
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print(f"Running subsample {i + 1} out of 10...")
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sample = random.choice(inputset)
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with progress.measure(id="encryption-time-ms", label="Encryption Time (ms)"):
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encrypted = circuit.encrypt(*sample)
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with progress.measure(id="evaluation-time-ms", label="Evaluation Time (ms)"):
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ran = circuit.run(encrypted)
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with progress.measure(id="decryption-time-ms", label="Decryption Time (ms)"):
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output = circuit.decrypt(ran)
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progress.measure(
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id="accuracy",
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label="Accuracy",
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value=int(output == function(*sample)),
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)
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