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Debug
In this section, you will learn how to debug the compilation process easily and find help in the case that you cannot resolve your issue.
There are two configuration options that you can use to understand what's happening under the hood during compilation process.
- compiler_verbose_mode will print the passes applied by the compiler and let you see the transformations done by the compiler. Also, in case of crashes, it could narrow down the crash location.
- compiler_debug_mode is a lot more detailed version of the verbose mode. Even better for crashes.
These flags might not work as expected in Jupyter notebooks as they output to stderr directly from C++.
Concrete has an artifact system to simplify the process of debugging issues.
In case of compilation failures, artifacts are exported automatically to the
.artifacts directory under the working directory. Let's intentionally create a compilation failure to show what is exported.def f(x):
return np.sin(x)
This function fails to compile because Concrete does not support floating-point outputs. When you try to compile it, an exception will be raised and the artifacts will be exported automatically. If you go to the
.artifacts directory under the working directory, you'll see the following files:This file contains information about your setup (i.e., your 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
This file contains information about Python packages and their versions installed on your system.
astroid==2.15.0
attrs==22.2.0
auditwheel==5.3.0
...
wheel==0.40.0
wrapt==1.15.0
zipp==3.15.0
This file contains information about the function you tried to compile.
def f(x):
return np.sin(x)
This file contains information about the encryption status of the parameters of the function you tried to compile.
x :: encrypted
This file contains the textual representation of the initial computation graph right after tracing.
%0 = x # EncryptedScalar<uint3>
%1 = sin(%0) # EncryptedScalar<float64>
return %1
This file contains the textual representation of the final computation graph right before MLIR conversion.
%0 = x # EncryptedScalar<uint3>
%1 = sin(%0) # EncryptedScalar<float64>
return %1
This file contains information about the error you received.
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/concrete/fhe/compilation/decorators.py", line 159, in compile
return self.compiler.compile(inputset, configuration, artifacts, **kwargs)
File "/usr/local/lib/python3.10/site-packages/concrete/fhe/compilation/compiler.py", line 437, in compile
mlir = GraphConverter.convert(self.graph)
File "/usr/local/lib/python3.10/site-packages/concrete/fhe/mlir/graph_converter.py", line 677, in convert
GraphConverter._check_graph_convertibility(graph)
File "/usr/local/lib/python3.10/site-packages/concrete/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 are mostly used for visualization. They can be very useful for demonstrations. Here is how to perform one:
from concrete 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()
If you go to the
/tmp/custom/export/path directory, you'll see the following files:This file contains 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
This file contains 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
This file contains 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
This file contains information about the MLIR of the function you compiled using the inputset you provided.
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>
}
}
This file contains information about the client parameters chosen by Concrete.
{
"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
}
]
}
If you cannot find a solution in the community forum, or you found a bug in the library, you could create an issue in our GitHub repository.
In case of a bug, try to:
- minimize randomness;
- minimize your function as much as possible while keeping the bug - this will help to fix the bug faster;
- include your inputset in the issue;
- include reproduction steps in the issue;
- include debug artifacts in the issue.
In case of a feature request, try to:
- give a minimal example of the desired behavior;
- explain your use case.
Last modified 1mo ago