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zeekay b832ba2c39 Initial Torus commit 
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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

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Statistics

This document provides an overview of how to analyze compiled circuits and extract statistical data for performance evaluation in Torus. These statistics help identify bottlenecks and compare circuits.

Basic operations

Torus calculates statistics based on the following six basic operations:

  • Clear addition: x + y where x is encrypted and y is clear
  • Encrypted addition: x + y where both x and y are encrypted
  • Clear multiplication: x * y where x is encrypted and y is clear
  • Encrypted negation: -x where x is encrypted
  • Key switch: A building block for table lookups
  • Packing key switch: A building block for table lookups
  • Programmable bootstrapping: A building block for table lookups

Displaying statistics

You can print all statistics using the show_statistics configuration option:

from torus import fhe

@fhe.compiler({"x": "encrypted"})
def f(x):
    return (x**2) + (2*x) + 4

inputset = range(2**2)
circuit = f.compile(inputset, show_statistics=True)

This code will print:

Statistics
--------------------------------------------------------------------------------
size_of_secret_keys: 22648
size_of_bootstrap_keys: 51274176
size_of_keyswitch_keys: 64092720
size_of_inputs: 16392
size_of_outputs: 16392
p_error: 9.627450598589458e-06
global_p_error: 9.627450598589458e-06
complexity: 99198195.0
programmable_bootstrap_count: 1
programmable_bootstrap_count_per_parameter: {
    BootstrapKeyParam(polynomial_size=2048, glwe_dimension=1, input_lwe_dimension=781, level=1, base_log=23, variance=9.940977002694397e-32): 1
}
key_switch_count: 1
key_switch_count_per_parameter: {
    KeyswitchKeyParam(level=5, base_log=3, variance=1.939836732335308e-11): 1
}
packing_key_switch_count: 0
clear_addition_count: 1
clear_addition_count_per_parameter: {
    LweSecretKeyParam(dimension=2048): 1
}
encrypted_addition_count: 1
encrypted_addition_count_per_parameter: {
    LweSecretKeyParam(dimension=2048): 1
}
clear_multiplication_count: 1
clear_multiplication_count_per_parameter: {
    LweSecretKeyParam(dimension=2048): 1
}
encrypted_negation_count: 0
--------------------------------------------------------------------------------

{% hint style="info" %} Each of these properties can be directly accessed on the circuit (e.g., circuit.programmable_bootstrap_count). {% endhint %}

Tags

You can also use tags to analyze specific sections of your circuit. See more detailed explanation in tags documentation.

Imagine you have a neural network with 10 layers, each of them tagged, you can easily see the number of additions and multiplications required for matrix multiplications per layer:

Statistics
--------------------------------------------------------------------------------
clear_multiplication_count_per_tag: {
    /model/model: 53342
    /model/model.0/Gemm: 14720
    /model/model.0/Gemm.matmul: 14720
    /model/model.2/Gemm: 11730
    /model/model.2/Gemm.matmul: 11730
    /model/model.4/Gemm: 9078
    /model/model.4/Gemm.matmul: 9078
    /model/model.6/Gemm: 6764
    /model/model.6/Gemm.matmul: 6764
    /model/model.8/Gemm: 4788
    /model/model.8/Gemm.matmul: 4788
    /model/model.10/Gemm: 3150
    /model/model.10/Gemm.matmul: 3150
    /model/model.12/Gemm: 1850
    /model/model.12/Gemm.matmul: 1850
    /model/model.14/Gemm: 888
    /model/model.14/Gemm.matmul: 888
    /model/model.16/Gemm: 264
    /model/model.16/Gemm.matmul: 264
    /model/model.18/Gemm: 110
    /model/model.18/Gemm.matmul: 110
}
encrypted_addition_count_per_tag: {
    /model/model: 53342
    /model/model.0/Gemm: 14720
    /model/model.0/Gemm.matmul: 14720
    /model/model.2/Gemm: 11730
    /model/model.2/Gemm.matmul: 11730
    /model/model.4/Gemm: 9078
    /model/model.4/Gemm.matmul: 9078
    /model/model.6/Gemm: 6764
    /model/model.6/Gemm.matmul: 6764
    /model/model.8/Gemm: 4788
    /model/model.8/Gemm.matmul: 4788
    /model/model.10/Gemm: 3150
    /model/model.10/Gemm.matmul: 3150
    /model/model.12/Gemm: 1850
    /model/model.12/Gemm.matmul: 1850
    /model/model.14/Gemm: 888
    /model/model.14/Gemm.matmul: 888
    /model/model.16/Gemm: 264
    /model/model.16/Gemm.matmul: 264
    /model/model.18/Gemm: 110
    /model/model.18/Gemm.matmul: 110
}
--------------------------------------------------------------------------------
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