Provides the infrastructure for implementing and late-binding
OpInterfaces from Python.
* On the mlir-c API declaration side, each `XOpInterface` has a callback
struct, with a callback for each method and a userdata member (provided
as an arg to each method), and a
`mlirXOpInterfaceAttachFallbackModel(ctx, op_name, callbacks)` func.
* This CAPI is implemented by defining a subclass of
`XOpInterface::FallbackModel` that holds the callback struct and has
each method call the corresponding callback (with userdata as an arg).
Given a callback struct, a new `FallbackModel` is created and attached,
i.e. late bound, to the named op. (MLIR's interface infrastructure is
such that the thus registered `FallbackModel` will be returned in case
the op gets cast to the `XOpInterface`.)
* On the Python side, we expose a stand-in `XOpInterface` base class
which has one (class)method: `XOpInterface.attach(cls, op_name, ctx)`.
Python users subclass this class (`class MyInterfaceImpl(XOpInterface):
...`) and implement the interface's methods (with the right names and
signatures). The user calls `attach` on the subclass
(`MyInterfaceImpl.attach("my_dialect.my_op", ctx)`) which prepares the
callbacks struct _with userdata set to the subclass_ (as we use it to
lookup methods). These callbacks (and userdata) are then registered as
an `XOpInterface::FallbackModel` by
`mlirXOpInterfaceAttachFallbackModel(...)`. From then on the Python
methods will be used to respond to calls to the interface methods
(originating in C++).
This PR enables implementing the TransformOpInterface and the
MemoryEffectsOpInterface, both of which are required for making an op
into a transform op.
Everything besides the above linked code is there to facilitate exposing
the interfaces: the right types for the arguments of the methods are
exposed as are functions/methods for manipulating these arguments (e.g.
specifying side effects on `OpOperand`s and `OpResult`s and being able
to access and set the transform handles associated with args and
results).
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