7.9 KiB
Compilation Artifacts
In this tutorial, we are going to go over the artifact system, which is designed to inspect/debug the compilation process easily.
Automatic export
In case of compilation failures, artifacts are exported automatically to .artifacts directory under the working directory. Let's intentionally create a compilation failure and show what kinds of things are exported.
def f(x):
return np.sin(x)
This function fails to compile because Concrete Numpy doesn't support floating point outputs. When you try to compile it (you might want to check this to see how you can do that), an exception will be raised and the artifacts will be exported automatically.
environment.txt
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
requirements.txt
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
function.txt
This file contains information about the function you are trying to compile.
def f(x):
return np.sin(x)
parameters.txt
This file contains information about the parameters of the function you are trying to compile.
x :: EncryptedScalar<Integer<unsigned, 3 bits>>
1.initial.graph.txt
This file contains textual representation of the initial computation graph right after tracing.
%0 = x # EncryptedScalar<uint3>
%1 = sin(%0) # EncryptedScalar<float64>
return %1
1.initial.graph.png
This file contains the visual representation of the initial computation graph right after tracing.
2.final.graph.txt
This file contains textual representation of the final computation graph right before MLIR conversion.
%0 = x # EncryptedScalar<uint3>
%1 = sin(%0) # EncryptedScalar<float64>
return %1
2.final.graph.png
This file contains the visual representation of the final computation graph right before MLIR conversion.
traceback.txt
This file contains information about the error you got.
Traceback (most recent call last):
File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/compile.py", line 141, in run_compilation_function_with_error_management
return compilation_function()
File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/compile.py", line 769, in compilation_function
return _compile_numpy_function_internal(
File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/compile.py", line 722, in _compile_numpy_function_internal
fhe_circuit = _compile_op_graph_to_fhe_circuit_internal(
File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/compile.py", line 626, in _compile_op_graph_to_fhe_circuit_internal
prepare_op_graph_for_mlir(op_graph)
File "/home/default/Documents/Projects/Zama/hdk/concrete/numpy/compile.py", line 597, in prepare_op_graph_for_mlir
raise RuntimeError(
RuntimeError: function you are trying to compile isn't supported for MLIR lowering
%0 = x # EncryptedScalar<uint3>
%1 = sin(%0) # EncryptedScalar<float64>
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ only integer outputs are supported
return %1
Manual export
Manual exports are mostly used for visualization. Nonetheless, they can be very useful for demonstrations. Here is how to do it:
import concrete.numpy as cnp
import numpy as np
import pathlib
artifacts = cnp.CompilationArtifacts("/tmp/custom/export/path")
@cnp.compiler({"x": "encrypted"}, artifacts=artifacts)
def f(x):
return 127 - (50 * (np.sin(x) + 1)).astype(np.int64)
f.compile(range(2 ** 3))
artifacts.export()
1.initial.graph.txt
This file contains 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<uint3>
%4 = sin(%3) # EncryptedScalar<float64>
%5 = add(%4, %2) # EncryptedScalar<float64>
%6 = mul(%5, %1) # EncryptedScalar<float64>
%7 = astype(%6, dtype=uint32) # EncryptedScalar<uint32>
%8 = sub(%0, %7) # EncryptedScalar<uint32>
return %8
1.initial.graph.png
This file contains the visual representation of the initial computation graph right after tracing.
2.after-float-fuse-0.graph.txt
This file contains textual representation of the intermediate computation graph after fusing.
%0 = 127 # ClearScalar<uint7>
%1 = x # EncryptedScalar<uint3>
%2 = subgraph(%1) # EncryptedScalar<uint32>
%3 = sub(%0, %2) # EncryptedScalar<uint32>
return %3
Subgraphs:
%2 = subgraph(%1):
%0 = 50 # ClearScalar<uint6>
%1 = 1 # ClearScalar<uint1>
%2 = float_subgraph_input # EncryptedScalar<uint3>
%3 = sin(%2) # EncryptedScalar<float64>
%4 = add(%3, %1) # EncryptedScalar<float64>
%5 = mul(%4, %0) # EncryptedScalar<float64>
%6 = astype(%5, dtype=uint32) # EncryptedScalar<uint32>
return %6
2.after-float-fuse-0.graph.png
This file contains the visual representation of the intermediate computation graph after fusing.
3.final.graph.txt
This file contains 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 = sub(%0, %2) # EncryptedScalar<uint7>
return %3
Subgraphs:
%2 = subgraph(%1):
%0 = 50 # ClearScalar<uint6>
%1 = 1 # ClearScalar<uint1>
%2 = float_subgraph_input # EncryptedScalar<uint3>
%3 = sin(%2) # EncryptedScalar<float64>
%4 = add(%3, %1) # EncryptedScalar<float64>
%5 = mul(%4, %0) # EncryptedScalar<float64>
%6 = astype(%5, dtype=uint32) # EncryptedScalar<uint7>
return %6
3.final.graph.png
This file contains the visual representation of the final computation graph right before MLIR conversion.
bounds.txt
This file contains information about the bounds of the final computation graph of the function you are trying to compile using the input set you provide.
%0 :: [127, 127]
%1 :: [0, 7]
%2 :: [2, 95]
%3 :: [32, 125]
You can learn what bounds are here.
mlir.txt
This file contains information about the MLIR of the function you are trying to compile using the input set you provide.
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>
}
}
You can learn more about MLIR here.