The main debugging function is
`TypeInferenceUtils::dumpAllState(mlir::Operation* op)` which dumps
the entire state of type inference for the function containing `op`.
The DAG pass establishing a mapping between operations in the IR and
the optimizer DAG currently omits assignment of the optimizer ID to
`FHE.reinterpret_precision` operations via the `TFHE.OId`
attribute. This prevents subsequent passes from determining to which
optimizer partition a `FHE.reinterpret_precision` operation belongs.
This commit removes the early exit in `FunctionToDag::addOperation`
for the handling of `FHE.reinterpret_precision` that prevented the
code assigning the optimizer ID from being executed.
On Mac arm, the c api backing the python bindings does not propagate the
exceptions properly to the concretelang python module. This makes all
exceptions raised through `CompilerEngine.cpp` fall in the catch-all
case of the pybind exceptions handler.
Since there is no particular need for a public c api, we just remove it
from the bindings, and move all the content of `CompilerEngine.cpp`
directly in the `CompilerAPIModule.cpp` file.
The current pass applying the parameters determined by the optimizer
to the IR propagates the parametrized TFHE types to operations not
directly tagged with an optimizer ID only under certain conditions. In
particular, it does not always properly propagate types into nested
regions (e.g., of `scf.for` loops).
This burdens preceding transformations that are applied in between the
invocation of the optimizer and the parametrization pass with
data-flow analysis and book-keeping in order to tag newly inserted
operations with the right optimizer IDs that ensure proper
parametrization.
This commit replaces the current parametrization pass with a new pass
that propagates parametrized TFHE types up and down def-use chains
using type inference and a proper rewriter. The pass is limited to the
operations supported by `TFHEParametrizationTypeResolver::resolve`.
In order to avoid leftover TFHE operations to be lowered further down
the pipeline after parametrization, run the canonicalizer, which
includes dead code eliminiation.
