[mlir] Move casting calls from methods to function calls

The MLIR classes Type/Attribute/Operation/Op/Value support
cast/dyn_cast/isa/dyn_cast_or_null functionality through llvm's doCast
functionality in addition to defining methods with the same name.
This change begins the migration of uses of the method to the
corresponding function call as has been decided as more consistent.

Note that there still exist classes that only define methods directly,
such as AffineExpr, and this does not include work currently to support
a functional cast/isa call.

Context:
- https://mlir.llvm.org/deprecation/ at "Use the free function variants
  for dyn_cast/cast/isa/…"
- Original discussion at https://discourse.llvm.org/t/preferred-casting-style-going-forward/68443

Implementation:
This patch updates all remaining uses of the deprecated functionality in
mlir/. This was done with clang-tidy as described below and further
modifications to GPUBase.td and OpenMPOpsInterfaces.td.

Steps are described per line, as comments are removed by git:
0. Retrieve the change from the following to build clang-tidy with an
   additional check:
   main...tpopp:llvm-project:tidy-cast-check
1. Build clang-tidy
2. Run clang-tidy over your entire codebase while disabling all checks
   and enabling the one relevant one. Run on all header files also.
3. Delete .inc files that were also modified, so the next build rebuilds
   them to a pure state.

```
ninja -C $BUILD_DIR clang-tidy

run-clang-tidy -clang-tidy-binary=$BUILD_DIR/bin/clang-tidy -checks='-*,misc-cast-functions'\
               -header-filter=mlir/ mlir/* -fix

rm -rf $BUILD_DIR/tools/mlir/**/*.inc
```

Differential Revision: https://reviews.llvm.org/D151542

mlir/lib/Dialect/SparseTensor/Transforms/SparseTensorCodegen.cpp

@@ -414,7 +414,7 @@ public:
   /// TODO: better unord/not-unique; also generalize, optimize, specialize!
   SmallVector<Value> genImplementation(TypeRange retTypes, ValueRange args,
                                        OpBuilder &builder, Location loc) {
-    const SparseTensorType stt(rtp.cast<RankedTensorType>());
+    const SparseTensorType stt(llvm::cast<RankedTensorType>(rtp));
     const Level lvlRank = stt.getLvlRank();
     // Extract fields and coordinates from args.
     SmallVector<Value> fields = llvm::to_vector(args.drop_back(lvlRank + 1));
@@ -466,7 +466,7 @@ public:
     // The mangled name of the function has this format:
     //   <namePrefix>_<DLT>_<shape>_<ordering>_<eltType>_<crdWidth>_<posWidth>
     constexpr const char kInsertFuncNamePrefix[] = "_insert_";
-    const SparseTensorType stt(rtp.cast<RankedTensorType>());
+    const SparseTensorType stt(llvm::cast<RankedTensorType>(rtp));
 
     SmallString<32> nameBuffer;
     llvm::raw_svector_ostream nameOstream(nameBuffer);
@@ -541,14 +541,14 @@ static void genEndInsert(OpBuilder &builder, Location loc,
 
 static TypedValue<BaseMemRefType> genToMemref(OpBuilder &builder, Location loc,
                                               Value tensor) {
-  auto tTp = tensor.getType().cast<TensorType>();
+  auto tTp = llvm::cast<TensorType>(tensor.getType());
   auto mTp = MemRefType::get(tTp.getShape(), tTp.getElementType());
   return builder.create<bufferization::ToMemrefOp>(loc, mTp, tensor)
       .getResult();
 }
 
 Value genSliceToSize(OpBuilder &builder, Location loc, Value mem, Value sz) {
-  auto elemTp = mem.getType().cast<MemRefType>().getElementType();
+  auto elemTp = llvm::cast<MemRefType>(mem.getType()).getElementType();
   return builder
       .create<memref::SubViewOp>(
           loc, MemRefType::get({ShapedType::kDynamic}, elemTp), mem,