It was decided to use ONNX as the intermediate format to convert various ML models (including torch nn.Module and various sklearn models, among others) to NumPy. The reason here is that converting/interpreting torchscript and other representations would require a lot of effort while ONNX has tools readily available to easily manipulate the model's representation in Python. Additionally, JAX had an example of a lightweight interpreter to run ONNX models as NumPy code.
In the diagram above, it is perfectly possible to stop at the NumpyModule level if you just want to run the torch model as NumPy code without doing quantization.
Note that if you keep the obtained NumpyModule without quantizing it with Post Training Quantization (PTQ), it is very likely that it won't be convertible to FHE since the Concrete stack requires operators to use integers for computations.
The NumpyModule stores the ONNX model that it interprets. The interpreter works by going through the ONNX graph (which, by specification, is sorted in topological order, allowing users to run through the graph without having to care for evaluation order) and storing the intermediate results as it goes. To execute a node, the interpreter feeds the required inputs - taken either from the model inputs or the intermediate results - to the NumPy implementation of each ONNX node.
Do note that the NumpyModule interpreter currently supports the following ONNX operators.
Initializers (ONNX's parameters) are quantized according to n_bits and passed to the Post Training Quantization (PTQ) process.
During the PTQ process, the ONNX model stored in the NumpyModule is interpreted and calibrated using the supported ONNX operators for PTQ.
Quantized operators are then used to create a QuantizedModule that, similarly to the NumpyModule, runs through the operators to perform the quantized inference with integers-only operations.
That QuantizedModule is then compilable to FHE if the intermediate values conform to the 7 bits precision limit of the Concrete stack.
QuantizedOpQuantizedOp is the base class for all ONNX quantized operators. It abstracts away a lot of things to allow easy implementation of new quantized ops.
You can check ops_impl.py to see how implementations are done in NumPy. The requirements are as follows:
The required inputs should be positional arguments only before the /, which marks the limit of the positional arguments
The optional inputs should be positional or keyword arguments between the / and *, which marks the limits of positional or keyword arguments
The operator attributes should be keyword arguments only after the *
The proper use of positional/keyword arguments is required to allow the QuantizedOp class to properly populate metadata automatically. It uses Python inspect modules and stores relevant information for each argument related to its positional/keyword status. This allows us to use our NumPy implementation as specifications for QuantizedOp, which removes some data duplication and allows us to have a single source of truth for QuantizedOp and ONNX NumPy implementations.
In that case (unless the quantized implementation requires special handling like QuantizedGemm), you can just set _impl_for_op_named to the name of the ONNX op for which the quantized class is implemented (this uses the mapping ONNX_OPS_TO_numpy_IMPL we have in onnx_utils.py to get the right implementation).
If you want to provide an alternative implementation, you can set _impl_for_op_named to the name of the operator (e.g. Exp) and you can set impl and/or q_impl to the functions that will do the alternative handling. QuantizedGemm is an example of such a case where quantized matrix multiplication requires proper handling of scales and zero points. The q_impl of that class reflects that.