[mlir][py] NFC: remove exception-based isa from linalg module (#92556)

When this code was written, we didn't have proper isinstance support for
operation classes in Python. Now we do, so there is no reason to keep
the expensive exception-based flow.

Author: ftynse

mlir/python/mlir/dialects/linalg/__init__.py

@@ -55,7 +55,6 @@
 #     TODO: guard against surprises and fail create Runtime Custom Ops with
 #     the same name as existing Core Named Ops.
 from .opdsl.ops.core_named_ops import *
-from .opdsl.lang.emitter import isa
 
 from ...ir import *
 from .._ods_common import get_op_result_or_value as _get_op_result_or_value
@@ -71,7 +70,7 @@ def transpose(
     if len(outs) > 1:
         raise ValueError(f"{outs=} must have length 1.")
     init = _get_op_result_or_value(outs[0])
-    result_types = [init.type] if isa(RankedTensorType, init.type) else []
+    result_types = [init.type] if isinstance(init.type, RankedTensorType) else []
 
     op = TransposeOp(
         result=result_types,
@@ -93,7 +92,7 @@ def broadcast(
     if len(outs) > 1:
         raise ValueError(f"{outs=} must have length 1.")
     init = _get_op_result_or_value(outs[0])
-    result_types = [init.type] if isa(RankedTensorType, init.type) else []
+    result_types = [init.type] if isinstance(init.type, RankedTensorType) else []
 
     op = BroadcastOp(
         result=result_types,

mlir/python/mlir/dialects/linalg/opdsl/lang/emitter.py

@@ -31,14 +31,6 @@
 ValueList = Union[Sequence[Value], OpResultList]
 
 
-def isa(cls: Type, ty: Type):
-    try:
-        cls(ty)
-        return True
-    except ValueError:
-        return False
-
-
 def prepare_common_structured_op(
     op_config: LinalgStructuredOpConfig,
     *ins: Value,
@@ -127,7 +119,7 @@ def prepare_common_structured_op(
         op_config, in_arg_defs, ins, out_arg_defs, outs
     )
 
-    result_types = [t for t in out_types if isa(RankedTensorType, t)]
+    result_types = [t for t in out_types if isinstance(t, RankedTensorType)]
 
     # Initialize the type dictionary with the predefined types.
     type_mapping = dict()  # type: Dict[str, Type]