Compilation
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Concrete-ML implements machine model inference using Concrete-Numpy as a backend. In order to execute in FHE, a numerical program written in Concrete-Numpy needs to be compiled. This functionality is , and Concrete-ML hides away most of the complexity of this step. The entire compilation process is done by Concrete-Numpy.
From the perspective of the Concrete-ML user, the compilation process performed by Concrete-Numpy can be broken up into 3 steps:
numpy program tracing and creation of a Concrete-Numpy op-graph
checking that the op-graph is FHE compatible
producing machine code for the op-graph. This step automatically determines cryptographic parameters
Additionally, the packages the result of the last step in a way that allows to deploy the encrypted circuit to a server and to perform key generation, encryption and decryption on the client side.
The first step in the list above takes a python function implemented using the Concrete-Numpy and transforms it into an executable operation graph. In this step all the floating point subgraphs in the op-graph are fused and converted to Table Lookup operations.
This enables to:
execute the op-graph, which includes TLUs, on clear non-encrypted data. This is, of course, not secure, but is much faster than executing in FHE. This mode is useful for debugging. This is called the Virtual Library.
verify the maximum bitwidth of the op-graph, to determine FHE compatibility, without actually compiling the circuit to machine code. This feature is available through Concrete-Numpy and is part of the overall .
The second step of compilation checks that the maximum bitwidth encountered anywhere in the circuit is valid.
If the check fails for a machine learning model, the user will need to tweak the available , and model hyperparameters in order to obtain FHE compatibility. The Virtual Library is useful in this setting, as described in the
Finally, the FHE compatible op-graph and the necessary cryptographic primitives from Concrete-Framework are converted to machine code.