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# Debug

In this section, you will learn how to debug the compilation process easily as well as how to get help in case you cannot resolve your issue.

**Concrete-Numpy**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 kinds of things are exported.def f(x):

return np.sin(x)

This function fails to compile because

**Concrete-Numpy**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 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.

alabaster==0.7.12

appdirs==1.4.4

argon2-cffi==21.1.0

...

wheel==0.37.0

widgetsnbextension==3.5.1

wrapt==1.12.1

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 visual representation of the initial computation graph right after tracing.

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 the visual representation of the final computation graph right before MLIR conversion.

This file contains information about the error you received.

Traceback (most recent call last):

File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/compilation/compiler.py", line 320, in compile

mlir = GraphConverter.convert(self.graph, virtual=self.configuration.virtual)

File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/mlir/graph_converter.py", line 298, in convert

GraphConverter._check_graph_convertibility(graph)

File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/mlir/graph_converter.py", line 175, in _check_graph_convertibility

raise RuntimeError(message)

RuntimeError: Function you are trying to compile cannot be converted to MLIR

%0 = x # EncryptedScalar<uint4>

%1 = sin(%0) # EncryptedScalar<float64>

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ only integer operations are supported

return %1

Manual exports are mostly used for visualization. Nonetheless, they can be very useful for demonstrations. Here is how to perform one:

import concrete.numpy as cnp

import numpy as np

artifacts = cnp.DebugArtifacts("/tmp/custom/export/path")

@cnp.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 = 127 # ClearScalar<uint7>

%1 = 50 # ClearScalar<uint6>

%2 = 1 # ClearScalar<uint1>

%3 = x # EncryptedScalar<uint1>

%4 = sin(%3) # EncryptedScalar<float64>

%5 = add(%4, %2) # EncryptedScalar<float64>

%6 = multiply(%1, %5) # EncryptedScalar<float64>

%7 = astype(%6, dtype=int_) # EncryptedScalar<uint1>

%8 = subtract(%0, %7) # EncryptedScalar<uint1>

return %8

This file contains the visual representation of the initial computation graph right after tracing.

This file contains the textual representation of the intermediate computation graph after fusing.

%0 = 127 # ClearScalar<uint7>

%1 = x # EncryptedScalar<uint1>

%2 = subgraph(%1) # EncryptedScalar<uint1>

%3 = subtract(%0, %2) # EncryptedScalar<uint1>

return %3

Subgraphs:

%2 = subgraph(%1):

%0 = 50 # ClearScalar<uint6>

%1 = 1 # ClearScalar<uint1>

%2 = input # EncryptedScalar<uint1>

%3 = sin(%2) # EncryptedScalar<float64>

%4 = add(%3, %1) # EncryptedScalar<float64>

%5 = multiply(%0, %4) # EncryptedScalar<float64>

%6 = astype(%5, dtype=int_) # EncryptedScalar<uint1>

return %6

This file contains the visual representation of the intermediate computation graph after fusing.

This file contains the textual representation of the final computation graph right before MLIR conversion.

%0 = 127 # ClearScalar<uint7>

%1 = x # EncryptedScalar<uint3>

%2 = subgraph(%1) # EncryptedScalar<uint7>

%3 = subtract(%0, %2) # EncryptedScalar<uint7>

return %3

Subgraphs:

%2 = subgraph(%1):

%0 = 50 # ClearScalar<uint6>

%1 = 1 # ClearScalar<uint1>

%2 = input # EncryptedScalar<uint1>

%3 = sin(%2) # EncryptedScalar<float64>

%4 = add(%3, %1) # EncryptedScalar<float64>

%5 = multiply(%0, %4) # EncryptedScalar<float64>

%6 = astype(%5, dtype=int_) # EncryptedScalar<uint1>

return %6

This file contains the visual representation of the final computation graph right before MLIR conversion.

This file contains information about the bounds of the final computation graph of the function you are compiling using the inputset you provide.

%0 :: [127, 127]

%1 :: [0, 7]

%2 :: [2, 95]

%3 :: [32, 125]

This file contains information about the MLIR of the function you compiled using the inputset you provided.

module {

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>

}

}

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
- try to minimize your function as much as possible while keeping the bug - this will help to fix the bug faster
- try to include your inputset in the issue
- try to include reproduction steps in the issue
- try to include debug artifacts in the issue

In case of a feature request:

- try to give a minimal example of the desired behavior
- try to explain your use case

Last modified 9mo ago