Following a rather direct approach to expose PDL usage from C and then
Python. This doesn't yes plumb through adding support for custom
matchers through this interface, so constrained to basics initially.

This also exposes greedy rewrite driver. Only way currently to define
patterns is via PDL (just to keep small). The creation of the PDL
pattern module could be improved to avoid folks potentially accessing
the module used to construct it post construction. No ergonomic work
done yet.

---------

Signed-off-by: Jacques Pienaar <jpienaar@google.com>

This PR adds "value casting", i.e., a mechanism to wrap `ir.Value` in a
proxy class that overloads dunders such as `__add__`, `__sub__`, and
`__mul__` for fun and great profit.

This is thematically similar to
https://github.com/llvm/llvm-project/commit/bfb1ba752655bf09b35c486f6cc9817dbedfb1bb
and
https://github.com/llvm/llvm-project/commit/9566ee280607d91fa2e5eca730a6765ac84dfd0f.
The example in the test demonstrates the value of the feature (no pun
intended):

```python
    @register_value_caster(F16Type.static_typeid)
    @register_value_caster(F32Type.static_typeid)
    @register_value_caster(F64Type.static_typeid)
    @register_value_caster(IntegerType.static_typeid)
    class ArithValue(Value):
        __add__ = partialmethod(_binary_op, op="add")
        __sub__ = partialmethod(_binary_op, op="sub")
        __mul__ = partialmethod(_binary_op, op="mul")

    a = arith.constant(value=FloatAttr.get(f16_t, 42.42))
    b = a + a
    # CHECK: ArithValue(%0 = arith.addf %cst, %cst : f16)
    print(b)

    a = arith.constant(value=FloatAttr.get(f32_t, 42.42))
    b = a - a
    # CHECK: ArithValue(%1 = arith.subf %cst_0, %cst_0 : f32)
    print(b)

    a = arith.constant(value=FloatAttr.get(f64_t, 42.42))
    b = a * a
    # CHECK: ArithValue(%2 = arith.mulf %cst_1, %cst_1 : f64)
    print(b)
```

**EDIT**: this now goes through the bindings and thus supports automatic
casting of `OpResult` (including as an element of `OpResultList`),
`BlockArgument` (including as an element of `BlockArgumentList`), as
well as `Value`.

This PR removes the various caching mechanisms currently in the python
bindings - both positive caching and negative caching.

This PR replaces the mixin `OpView` extension mechanism with the
standard inheritance mechanism.

Why? Firstly, mixins are not very pythonic (inheritance is usually used
for this), a little convoluted, and too "tight" (can only be used in the
immediately adjacent `_ext.py`). Secondly, it (mixins) are now blocking
are correct implementation of "value builders" (see
[here](https://github.com/llvm/llvm-project/pull/68764)) where the
problem becomes how to choose the correct base class that the value
builder should call.

This PR looks big/complicated but appearances are deceiving; 4 things
were needed to make this work:

1. Drop `skipDefaultBuilders` in
`OpPythonBindingGen::emitDefaultOpBuilders`
2. Former mixin extension classes are converted to inherit from the
generated `OpView` instead of being "mixins"
a. extension classes that simply were calling into an already generated
`super().__init__` continue to do so
b. (almost all) extension classes that were calling `self.build_generic`
because of a lack of default builder being generated can now also just
call `super().__init__`
3. To handle the [lone single
use-case](https://sourcegraph.com/search?q=context%3Aglobal+select_opview_mixin&patternType=standard&sm=1&groupBy=repo)
of `select_opview_mixin`, namely
[linalg](https://github.com/llvm/llvm-project/blob/main/mlir/python/mlir/dialects/_linalg_ops_ext.py#L38),
only a small change was necessary in `opdsl/lang/emitter.py` (thanks to
the emission/generation of default builders/`__init__`s)
4. since the `extend_opview_class` decorator is removed, we need a way
to register extension classes as the desired `OpView` that `op.opview`
conjures into existence; so we do the standard thing and just enable
replacing the existing registered `OpView` i.e.,
`register_operation(_Dialect, replace=True)`.

Note, the upgrade path for the common case is to change an extension to
inherit from the generated builder and decorate it with
`register_operation(_Dialect, replace=True)`. In the slightly more
complicated case where `super().__init(self.build_generic(...))` is
called in the extension's `__init__`, this needs to be updated to call
`__init__` in `OpView`, i.e., the grandparent (see updated docs). 
Note, also `<DIALECT>_ext.py` files/modules will no longer be automatically loaded.

Note, the PR has 3 base commits that look funny but this was done for
the purpose of tracking the line history of moving the
`<DIALECT>_ops_ext.py` class into `<DIALECT>.py` and updating (commit
labeled "fix").

depends on D150839

This diff uses `MlirTypeID` to register `TypeCaster`s (i.e., `[](PyType pyType) -> DerivedTy { return pyType; }`) for all concrete types (i.e., `PyConcrete<...>`) that are then queried for (by `MlirTypeID`) and called in `struct type_caster<MlirType>::cast`. The result is that anywhere an `MlirType mlirType` is returned from a python binding, that `mlirType` is automatically cast to the correct concrete type. For example:

```
      c0 = arith.ConstantOp(f32, 0.0)
      # CHECK: F32Type(f32)
      print(repr(c0.result.type))

      unranked_tensor_type = UnrankedTensorType.get(f32)
      unranked_tensor = tensor.FromElementsOp(unranked_tensor_type, [c0]).result

      # CHECK: UnrankedTensorType
      print(type(unranked_tensor.type).__name__)
      # CHECK: UnrankedTensorType(tensor<*xf32>)
      print(repr(unranked_tensor.type))
```

This functionality immediately extends to typed attributes (i.e., `attr.type`).

The diff also implements similar functionality for `mlir_type_subclass`es but in a slightly different way - for such types (which have no cpp corresponding `class` or `struct`) the user must provide a type caster in python (similar to how `AttrBuilder` works) or in cpp as a `py::cpp_function`.

Reviewed By: ftynse

Differential Revision: https://reviews.llvm.org/D150927

The raw `OpView` classes are used to bypass the constructors of `OpView`
subclasses, but having a separate class can create some confusing
behaviour, e.g.:
```
op = MyOp(...)
# fails, lhs is 'MyOp', rhs is '_MyOp'
assert type(op) == type(op.operation.opview)
```

Instead we can use `__new__` to achieve the same thing without a
separate class:
```
my_op = MyOp.__new__(MyOp)
OpView.__init__(my_op, op)
```

Reviewed By: stellaraccident

Differential Revision: https://reviews.llvm.org/D143830

Historically, the bindings for the Linalg dialect were included into the
"core" bindings library because they depended on the C++ implementation
of the "core" bindings. The other dialects followed the pattern. Now
that this dependency is gone, split out each dialect into a separate
Python extension library.

Depends On D116649, D116605

Reviewed By: stellaraccident

Differential Revision: https://reviews.llvm.org/D116662

So far, only the custom dialect types are exposed.

The build and packaging is same as for Linalg and SparseTensor, and in
need of refactoring that is beyond the scope of this patch.

Reviewed By: stellaraccident

Differential Revision: https://reviews.llvm.org/D116605

There is no completely automated facility for generating stubs that are both accurate and comprehensive for native modules. After some experimentation, I found that MyPy's stubgen does the best at generating correct stubs with a few caveats that are relatively easy to fix:
  * Some types resolve to cross module symbols incorrectly.
  * staticmethod and classmethod signatures seem to always be completely generic and need to be manually provided.
  * It does not generate an __all__ which, from testing, causes namespace pollution to be visible to IDE code completion.

As a first step, I did the following:
  * Ran `stubgen` for `_mlir.ir`, `_mlir.passmanager`, and `_mlirExecutionEngine`.
  * Manually looked for all instances where unnamed arguments were being emitted (i.e. as 'arg0', etc) and updated the C++ side to include names (and re-ran stubgen to get a good initial state).
  * Made/noted a few structural changes to each `pyi` file to make it minimally functional.
  * Added the `pyi` files to the CMake rules so they are installed and visible.

To test, I added a `.env` file to the root of the project with `PYTHONPATH=...` set as per instructions. Then reload the developer window (in VsCode) and verify that completion works for various changes to test cases.

There are still a number of overly generic signatures, but I want to check in this low-touch baseline before iterating on more ambiguous changes. This is already a big improvement.

Differential Revision: https://reviews.llvm.org/D114679

Introduce the initial support for operation interfaces in C API and Python
bindings. Interfaces are a key component of MLIR's extensibility and should be
available in bindings to make use of full potential of MLIR.

This initial implementation exposes InferTypeOpInterface all the way to the
Python bindings since it can be later used to simplify the operation
construction methods by inferring their return types instead of requiring the
user to do so. The general infrastructure for binding interfaces is defined and
InferTypeOpInterface can be used as an example for binding other interfaces.

Reviewed By: gysit

Differential Revision: https://reviews.llvm.org/D111656