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The MLIR dialect, its conversion passes (TFHEToConcrete, ConcreteToCAPI, ConcreteToLLVM), the optimizer component, the compiler binary, the Rust crate and the program_info parameter file all still carried the upstream project's name. They now carry this one's. Two things deliberately keep the word, because in them it is not the name. mlir::OpTrait CRTP parameters mean "the derived type", so they are spelled Derived and OpTy — LLVM's other conventional spelling for the same idiom. concreteShape is a capnp field: renaming it would change the serialized protocol, and the prose describing it is ordinary English. This also repairs references the earlier partial rename left dangling: Security.cpp included concrete/curves.gen.h and the directory is torus/, torus-cpu.h included concrete-num_complex.h, the Torus dialect CMakeLists named ConcreteOps.td, and compiler/setup.py asked find_namespace_packages for a "concrete" package that no longer exists — so it built without the package it ships.
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Debug
This document provides guidance on debugging the compilation process.
Compiler debug and verbose modes
Two configuration options are available to help you understand the compilation process:
- compiler_verbose_mode: Prints the compiler passes and shows the transformations applied. It can help identify the crash location if a crash occurs.
- compiler_debug_mode: A more detailed version of the verbose mode, providing additional information, particularly useful for diagnosing crashes.
{% hint style="warning" %}
These flags might not work as expected in Jupyter notebooks as they output to stderr directly from C++.
{% endhint %}
Debug artifacts
Torus includes an artifact system that simplifies the debugging process by automatically or manually exporting detailed information during compilation failures.
Automatic export
When a compilation fails, artifacts are automatically exported to the .artifacts directory in the working directory. Here's an example of what gets exported when a function fails to compile:
def f(x):
return np.sin(x)
This function fails to compile because Torus does not support floating-point outputs. When you try to compile it, an exception will be raised and the artifacts will be exported automatically. The following files will be generated in the .artifacts directory:
environment.txt: Information about your system setup, including the operating system and Python version.
Linux-5.12.13-arch1-2-x86_64-with-glibc2.29 #1 SMP PREEMPT Fri, 25 Jun 2021 22:56:51 +0000
Python 3.8.10
requirements.txt: The installed Python packages and their versions.
astroid==2.15.0
attrs==22.2.0
auditwheel==5.3.0
...
wheel==0.40.0
wrapt==1.15.0
zipp==3.15.0
function.txt: The code of the function that failed to compile.
def f(x):
return np.sin(x)
parameters.txt: Information about the encryption status function's parameters.
x :: encrypted
1.initial.graph.txt: The textual representation of the initial computation graph right after tracing.
%0 = x # EncryptedScalar<uint3>
%1 = sin(%0) # EncryptedScalar<float64>
return %1
final.graph.txt: The textual representation of the final computation graph right before MLIR conversion.
%0 = x # EncryptedScalar<uint3>
%1 = sin(%0) # EncryptedScalar<float64>
return %1
traceback.txt: Details of the error occurred.
Traceback (most recent call last):
File "/path/to/your/script.py", line 9, in <module>
circuit = f.compile(inputset)
File "/usr/local/lib/python3.10/site-packages/torus/fhe/compilation/decorators.py", line 159, in compile
return self.compiler.compile(inputset, configuration, artifacts, **kwargs)
File "/usr/local/lib/python3.10/site-packages/torus/fhe/compilation/compiler.py", line 437, in compile
mlir = GraphConverter.convert(self.graph)
File "/usr/local/lib/python3.10/site-packages/torus/fhe/mlir/graph_converter.py", line 677, in convert
GraphConverter._check_graph_convertibility(graph)
File "/usr/local/lib/python3.10/site-packages/torus/fhe/mlir/graph_converter.py", line 240, in _check_graph_convertibility
raise RuntimeError(message)
RuntimeError: Function you are trying to compile cannot be converted to MLIR
%0 = x # EncryptedScalar<uint3> ∈ [3, 5]
%1 = sin(%0) # EncryptedScalar<float64> ∈ [-0.958924, 0.14112]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ only integer operations are supported
/path/to/your/script.py:6
return %1
Manual exports
Manual exports are mostly used for visualization and demonstrations. Here is how to perform one:
from torus import fhe
import numpy as np
artifacts = fhe.DebugArtifacts("/tmp/custom/export/path")
@fhe.compiler({"x": "encrypted"})
def f(x):
return 127 - (50 * (np.sin(x) + 1)).astype(np.int64)
inputset = range(2 ** 3)
circuit = f.compile(inputset, artifacts=artifacts)
artifacts.export()
After running the code, you will find the following files under /tmp/custom/export/path directory:
1.initial.graph.txt: The textual representation of the initial computation graph right after tracing.
%0 = x # EncryptedScalar<uint1>
%1 = sin(%0) # EncryptedScalar<float64>
%2 = 1 # ClearScalar<uint1>
%3 = add(%1, %2) # EncryptedScalar<float64>
%4 = 50 # ClearScalar<uint6>
%5 = multiply(%4, %3) # EncryptedScalar<float64>
%6 = astype(%5, dtype=int_) # EncryptedScalar<uint1>
%7 = 127 # ClearScalar<uint7>
%8 = subtract(%7, %6) # EncryptedScalar<uint1>
return %8
2.after-fusing.graph.txt: The textual representation of the intermediate computation graph after fusing.
%0 = x # EncryptedScalar<uint1>
%1 = subgraph(%0) # EncryptedScalar<uint1>
%2 = 127 # ClearScalar<uint7>
%3 = subtract(%2, %1) # EncryptedScalar<uint1>
return %3
Subgraphs:
%1 = subgraph(%0):
%0 = input # EncryptedScalar<uint1>
%1 = sin(%0) # EncryptedScalar<float64>
%2 = 1 # ClearScalar<uint1>
%3 = add(%1, %2) # EncryptedScalar<float64>
%4 = 50 # ClearScalar<uint6>
%5 = multiply(%4, %3) # EncryptedScalar<float64>
%6 = astype(%5, dtype=int_) # EncryptedScalar<uint1>
return %6
3.final.graph.txt: The textual representation of the final computation graph right before MLIR conversion.
%0 = x # EncryptedScalar<uint3> ∈ [0, 7]
%1 = subgraph(%0) # EncryptedScalar<uint7> ∈ [2, 95]
%2 = 127 # ClearScalar<uint7> ∈ [127, 127]
%3 = subtract(%2, %1) # EncryptedScalar<uint7> ∈ [32, 125]
return %3
Subgraphs:
%1 = subgraph(%0):
%0 = input # EncryptedScalar<uint1>
%1 = sin(%0) # EncryptedScalar<float64>
%2 = 1 # ClearScalar<uint1>
%3 = add(%1, %2) # EncryptedScalar<float64>
%4 = 50 # ClearScalar<uint6>
%5 = multiply(%4, %3) # EncryptedScalar<float64>
%6 = astype(%5, dtype=int_) # EncryptedScalar<uint1>
return %6
mlir.txt: Information about the MLIR of the function which was compiled using the provided input-set.
module {
func.func @main(%arg0: !FHE.eint<7>) -> !FHE.eint<7> {
%c127_i8 = arith.constant 127 : i8
%cst = arith.constant dense<"..."> : tensor<128xi64>
%0 = "FHE.apply_lookup_table"(%arg0, %cst) : (!FHE.eint<7>, tensor<128xi64>) -> !FHE.eint<7>
%1 = "FHE.sub_int_eint"(%c127_i8, %0) : (i8, !FHE.eint<7>) -> !FHE.eint<7>
return %1 : !FHE.eint<7>
}
}
client\_parameters.json: Information about the client parameters chosen by Torus.
{
"bootstrapKeys": [
{
"baseLog": 22,
"glweDimension": 1,
"inputLweDimension": 908,
"inputSecretKeyID": 1,
"level": 1,
"outputSecretKeyID": 0,
"polynomialSize": 8192,
"variance": 4.70197740328915e-38
}
],
"functionName": "main",
"inputs": [
{
"encryption": {
"encoding": {
"isSigned": false,
"precision": 7
},
"secretKeyID": 0,
"variance": 4.70197740328915e-38
},
"shape": {
"dimensions": [],
"sign": false,
"size": 0,
"width": 7
}
}
],
"keyswitchKeys": [
{
"baseLog": 3,
"inputSecretKeyID": 0,
"level": 6,
"outputSecretKeyID": 1,
"variance": 1.7944329123150665e-13
}
],
"outputs": [
{
"encryption": {
"encoding": {
"isSigned": false,
"precision": 7
},
"secretKeyID": 0,
"variance": 4.70197740328915e-38
},
"shape": {
"dimensions": [],
"sign": false,
"size": 0,
"width": 7
}
}
],
"packingKeyswitchKeys": [],
"secretKeys": [
{
"dimension": 8192
},
{
"dimension": 908
}
]
}
Asking the community
You can seek help with your issue by asking a question directly in the community forum.
Submitting an issue
If you cannot find a solution in the community forum, or if you have found a bug in the library, you could create an issue in our GitHub repository.
For bug reports, try to:
- Avoid randomness to ensure reproducibility of the bug
- Minimize your function while keeping the bug to expedite the fix
- Include your input-set in the issue
- Provide clear reproduction steps
- Include debug artifacts in the issue
For feature requests, try to:
- Give a minimal example of the desired behavior
- Explain your use case