[mlir] make structured transform ops use types

Types have been introduced a while ago and provide for better
readability and transform-time verification. Use them in the ops from
the structured transform dialect extension.

In most cases, the types are appended as trailing functional types or a
derived format of the functional type that allows for an empty right
hand size without the annoying `-> ()` syntax (similarly to `func.func`
declaration that may omit the arrow). When handles are used inside mixed
static/dynamic lists, such as tile sizes, types of those handles follow
them immediately as in `sizes [%0 : !transform.any_value, 42]`. This
allows for better readability than matching the trailing type.

Update code to remove hardcoded PDL dependencies and expunge PDL from
structured transform op code.

Reviewed By: nicolasvasilache

Differential Revision: https://reviews.llvm.org/D144515

46 files changed, 1304 insertions, 1123 deletions

diff --git a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
index c7bc3767b27c..4f78b7d6c80d 100644
--- a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
@@ -14,7 +14,6 @@ include "mlir/Dialect/Transform/IR/TransformAttrs.td"
 include "mlir/Dialect/Transform/IR/TransformDialect.td"
 include "mlir/Dialect/Transform/IR/TransformInterfaces.td"
 include "mlir/Dialect/Transform/IR/TransformTypes.td"
-include "mlir/Dialect/PDL/IR/PDLTypes.td"
 include "mlir/Dialect/SCF/IR/DeviceMappingInterface.td"
 include "mlir/Interfaces/SideEffectInterfaces.td"
 include "mlir/IR/OpBase.td"
@@ -96,15 +95,16 @@ def DecomposeOp : Op<Transform_Dialect, "structured.decompose",
     #### Return modes
 
     This operation ignores non-Linalg ops and drops them in the return.
-    If all the operations referred to by the `target` PDLOperation decompose
+    If all the operations referred to by the `target` handle decompose
     properly, the transform succeeds. Otherwise the transform silently fails.
     The return handle points to only the subset of successfully produced
     computational operations, which can be empty.
   }];
 
-  let arguments = (ins PDL_Operation:$target);
-  let results = (outs PDL_Operation:$transformed);
-  let assemblyFormat = "$target attr-dict";
+  let arguments = (ins TransformHandleTypeInterface:$target);
+  let results = (outs TransformHandleTypeInterface:$transformed);
+  let assemblyFormat =
+      "$target attr-dict `:` functional-type(operands, results)";
 
   let extraClassDeclaration = [{
     ::mlir::DiagnosedSilenceableFailure applyToOne(
@@ -127,11 +127,11 @@ def FuseOp : Op<Transform_Dialect, "structured.fuse",
   }];
 
   let arguments =
-    (ins PDL_Operation:$target,
+    (ins TransformHandleTypeInterface:$target,
          DefaultValuedAttr<I64ArrayAttr, "{}">:$tile_sizes,
          DefaultValuedAttr<I64ArrayAttr, "{}">:$tile_interchange);
-  let results = (outs PDL_Operation:$transformed,
-                      Variadic<PDL_Operation>:$loops);
+  let results = (outs TransformHandleTypeInterface:$transformed,
+                      Variadic<TransformHandleTypeInterface>:$loops);
 
   let hasCustomAssemblyFormat = 1;
   let hasVerifier = 1;
@@ -181,10 +181,11 @@ def FuseIntoContainingOp :
     This operation only reads the containing op handle.
   }];
 
-  let arguments = (ins PDL_Operation:$producer_op,
-                       PDL_Operation:$containing_op);
-  let results = (outs PDL_Operation:$fused_op);
-  let assemblyFormat = "$producer_op `into` $containing_op attr-dict";
+  let arguments = (ins TransformHandleTypeInterface:$producer_op,
+                       TransformHandleTypeInterface:$containing_op);
+  let results = (outs TransformHandleTypeInterface:$fused_op);
+  let assemblyFormat = "$producer_op `into` $containing_op attr-dict "
+                       " `:` functional-type(operands, results)";
 
   let builders = [
     OpBuilder<(ins "Value":$producerOp, "Value":$containingOp)>
@@ -205,16 +206,18 @@ def GeneralizeOp : Op<Transform_Dialect, "structured.generalize",
     #### Return modes
 
     This operation ignores non-Linalg ops and drops them in the return.
-    If all the operations referred to by the `target` PDLOperation generalize
+    If all the operations referred to by the `target` handle generalize
     properly, the transform succeeds. Otherwise the transform silently fails.
     The return handle points to only the subset of successfully produced
     equivalent generic operations, which can be empty or contain the original
     ops if they were already in generic form.
   }];
 
-  let arguments = (ins PDL_Operation:$target);
-  let results = (outs PDL_Operation:$transformed);
-  let assemblyFormat = "$target attr-dict";
+  let arguments = (ins TransformHandleTypeInterface:$target);
+  let results = (outs TransformHandleTypeInterface:$transformed);
+  let assemblyFormat =
+      "$target attr-dict `:` "
+      "custom<SemiFunctionType>(type($target), type($transformed))";
 
   let extraClassDeclaration = [{
     ::mlir::DiagnosedSilenceableFailure applyToOne(
@@ -239,7 +242,7 @@ def InterchangeOp : Op<Transform_Dialect, "structured.interchange",
 
     This operation ignores non-linalg::Generic ops and drops them in the return.
     This operation fails if the interchange attribute is invalid.
-    If all the operations referred to by the `target` PDLOperation interchange
+    If all the operations referred to by the `target` handle interchange
     properly, the transform succeeds.
     If any interchange fails, the transform definitely fails.
     The return handle points to only the subset of successfully produced
@@ -247,14 +250,15 @@ def InterchangeOp : Op<Transform_Dialect, "structured.interchange",
   }];
 
   let arguments =
-    (ins PDL_Operation:$target,
+    (ins TransformHandleTypeInterface:$target,
          ConfinedAttr<DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">,
                       [DenseArrayNonNegative<DenseI64ArrayAttr>]>:$iterator_interchange);
-  let results = (outs PDL_Operation:$transformed);
+  let results = (outs TransformHandleTypeInterface:$transformed);
 
   let assemblyFormat = [{ 
     $target 
     (`iterator_interchange` `=` $iterator_interchange^)? attr-dict
+    `:` custom<SemiFunctionType>(type($target), type($transformed))
   }];
   let hasVerifier = 1;
 
@@ -552,13 +556,14 @@ def PackOp : Op<Transform_Dialect, "structured.pack", [
   }];
 
   let arguments = (ins TransformHandleTypeInterface:$target,
-                   Variadic<PDL_Operation>:$packed_sizes,
+                   Variadic<TransformHandleTypeInterface>:$packed_sizes,
                    DefaultValuedAttr<DenseI64ArrayAttr, "{}">:$static_packed_sizes);
   let results = (outs TransformHandleTypeInterface:$packed_op);
   let assemblyFormat = [{
     $target 
     `packed_sizes` `=` custom<DynamicIndexList>($packed_sizes,
-                                                $static_packed_sizes)
+                                                $static_packed_sizes,
+                                                type($packed_sizes))
     attr-dict
     `:` functional-type($target, results)
   }];
@@ -637,7 +642,7 @@ def PackGreedilyOp : Op<Transform_Dialect, "structured.pack_greedily", [
 
   // TODO: Transform_ConcreteOpType<linalg::LinalgOp> needs interface.
   let arguments = (ins TransformHandleTypeInterface:$target,
-                   Variadic<PDL_Operation>:$matmul_packed_sizes,
+                   Variadic<TransformHandleTypeInterface>:$matmul_packed_sizes,
                    ConfinedAttr<DefaultValuedAttr<DenseI64ArrayAttr, "{}">,
                                  [DenseArrayCount<3>]>:$static_matmul_packed_sizes,
                    ConfinedAttr<DefaultValuedAttr<DenseI64ArrayAttr, "{}">,
@@ -662,7 +667,8 @@ def PackGreedilyOp : Op<Transform_Dialect, "structured.pack_greedily", [
     $target
     oilist(
       `matmul_packed_sizes` `=` custom<DynamicIndexList>($matmul_packed_sizes,
-                                                         $static_matmul_packed_sizes)
+                                                         $static_matmul_packed_sizes,
+                                                         type($matmul_packed_sizes))
       (`matmul_padded_sizes_next_multiple_of` `=` 
         $matmul_padded_sizes_next_multiple_of^)?
       `matmul_inner_dims_order` `=` $matmul_inner_dims_order
@@ -758,23 +764,25 @@ def PadOp : Op<Transform_Dialect, "structured.pad",
     This operation ignores non-Linalg ops and drops them in the return.
     This operation may produce a definiteFailure if the padding fails for any
     reason.
-    If all the operations referred to by the `target` PDLOperation pad
+    If all the operations referred to by the `target` handle pad
     properly, the transform succeeds. Otherwise the transform silently fails.
     The return handle points to only the subset of successfully produced
     padded operations, which can be empty.
   }];
 
   let arguments =
-    (ins PDL_Operation:$target,
+    (ins TransformHandleTypeInterface:$target,
          DefaultValuedAttr<ArrayAttr, "{}">:$padding_values,
          DefaultValuedAttr<I64ArrayAttr, "{}">:$padding_dimensions,
          DefaultValuedAttr<I64ArrayAttr, "{}">:$pack_paddings,
          DefaultValuedAttr<
           TypedArrayAttrBase<I64ArrayAttr, "array of arrays of i64">,
           "{}">:$transpose_paddings);
-  let results = (outs PDL_Operation:$transformed);
+  let results = (outs TransformHandleTypeInterface:$transformed);
 
-  let assemblyFormat = "$target attr-dict";
+  let assemblyFormat =
+    "$target attr-dict `:` "
+    "custom<SemiFunctionType>(type($target), type($transformed))";
   let hasVerifier = 1;
 
   let extraClassDeclaration = [{
@@ -898,23 +906,25 @@ def PromoteOp : Op<Transform_Dialect, "structured.promote",
     This operation applies to a single Linalg op that satisfies the
     `promoteSubviewsPrecondition`, otherwise it fails.
 
-    If the operations referred to by the `target` PDLOperation promote
+    If the operations referred to by the `target` handle promote
     properly, the transform succeeds.
 
     When successful, the return handle points to the $target operation that
     was modified inplace.
   }];
 
-  let arguments = (ins PDL_Operation:$target,
+  let arguments = (ins TransformHandleTypeInterface:$target,
                        DefaultValuedAttr<I64ArrayAttr, "{}">:$operands_to_promote,
                        DefaultValuedAttr<BoolArrayAttr, "{}">:$use_full_tile_buffers,
                        UnitAttr:$use_full_tiles_by_default,
                        UnitAttr:$use_alloca,
                        OptionalAttr<DeviceMappingArrayAttr>:$mapping,
                        OptionalAttr<I64Attr>:$alignment);
-  let results = (outs PDL_Operation:$transformed);
+  let results = (outs TransformHandleTypeInterface:$transformed);
 
-  let assemblyFormat = "$target attr-dict";
+  let assemblyFormat =
+    "$target attr-dict `:`"
+    "custom<SemiFunctionType>(type($target), type($transformed))";
 
   let extraClassDeclaration = [{
     ::mlir::DiagnosedSilenceableFailure applyToOne(
@@ -943,10 +953,12 @@ def ReplaceOp : Op<Transform_Dialect, "structured.replace",
     This operation consumes the `target` handle.
   }];
 
-  let arguments = (ins PDL_Operation:$target);
-  let results = (outs PDL_Operation:$replacement);
+  let arguments = (ins TransformHandleTypeInterface:$target);
+  let results = (outs TransformHandleTypeInterface:$replacement);
   let regions = (region SizedRegion<1>:$bodyRegion);
-  let assemblyFormat = "$target attr-dict-with-keyword regions";
+  let assemblyFormat =
+      "$target attr-dict-with-keyword regions `:` "
+      "custom<SemiFunctionType>(type($target), type($replacement))";
   let hasVerifier = 1;
 }
 
@@ -966,7 +978,7 @@ def ScalarizeOp : Op<Transform_Dialect, "structured.scalarize",
     This operation ignores non-Linalg ops and drops them in the return.
     This operation produces `definiteFailure` if the scalarization fails for any
     reason.
-    If all the operations referred to by the `target` PDLOperation scalarize
+    If all the operations referred to by the `target` handle scalarize
     properly, the transform succeeds. Otherwise the transform silently fails.
 
     The return handle points to only the subset of successfully produced
@@ -980,10 +992,12 @@ def ScalarizeOp : Op<Transform_Dialect, "structured.scalarize",
     needed.
   }];
 
-  let arguments = (ins PDL_Operation:$target);
-  let results = (outs PDL_Operation:$result);
+  let arguments = (ins TransformHandleTypeInterface:$target);
+  let results = (outs TransformHandleTypeInterface:$result);
 
-  let assemblyFormat = "$target attr-dict";
+  let assemblyFormat = 
+    "$target attr-dict `:`"
+    "custom<SemiFunctionType>(type($target), type($result))";
 
   let extraClassDeclaration = [{
     ::mlir::DiagnosedSilenceableFailure applyToOne(
@@ -1016,13 +1030,13 @@ def RewriteInDestinationPassingStyleOp : Op<
     #### Return modes
 
     This operation ignores non-unsupported ops and drops them from the return.
-    If all the operations referred to by the `target` PDLOperation generalize
+    If all the operations referred to by the `target` handle generalize
     properly, the transform succeeds. Otherwise the transform silently fails.
     The return handle points to a subset of successfully produced operations:
-      - tensor.pad case, the returned handle points to the tensor.insert_slice.
-      - tensor.generate case, the returned handle points to the linalg.generic.
-      - tensor.from_elements case, the returned handle points to the last 
-        tensor.insert.
+      - `tensor.pad` case, the returned handle points to the tensor.insert_slice.
+      - `tensor.generate` case, the returned handle points to the linalg.generic.
+      - `tensor.from_elements` case, the returned handle points to the last 
+        `tensor.insert`.
   }];
 
   let arguments = (ins TransformHandleTypeInterface:$target);
@@ -1110,7 +1124,7 @@ def SplitReductionOp : Op<Transform_Dialect, "structured.split_reduction",
     This operation produces `definiteFailure` if the splitting fails for any
     reason.
 
-    If all the operations referred to by the `target` PDLOperation split
+    If all the operations referred to by the `target` handle split
     properly, the transform succeeds. Otherwise the transform silently fails.
     The 4 returned handles points to only the subset of successfully produced
     computational operations, which can all be empty.
@@ -1219,18 +1233,20 @@ def SplitReductionOp : Op<Transform_Dialect, "structured.split_reduction",
     ```
   }];
 
-  let arguments = (ins PDL_Operation:$target,
+  let arguments = (ins TransformHandleTypeInterface:$target,
                    DefaultValuedAttr<I64Attr, "{}">:$split_factor,
                    DefaultValuedAttr<I64Attr, "{}">:$insert_split_dimension,
                    UnitAttr:$inner_parallel,
                    UnitAttr:$use_scaling_algorithm,
                    UnitAttr:$use_alloc);
-  let results = (outs PDL_Operation:$init_or_alloc_op,
-                      PDL_Operation:$fill_op,
-                      PDL_Operation:$split_linalg_op,
-                      PDL_Operation:$combining_linalg_op);
+  let results = (outs TransformHandleTypeInterface:$init_or_alloc_op,
+                      TransformHandleTypeInterface:$fill_op,
+                      TransformHandleTypeInterface:$split_linalg_op,
+                      TransformHandleTypeInterface:$combining_linalg_op);
 
-  let assemblyFormat = "$target attr-dict";
+  let assemblyFormat = 
+      "$target attr-dict `:`"
+      "functional-type(operands, results)";
 
   let builders = [
     OpBuilder<(ins "Value":$target,
@@ -1326,12 +1342,12 @@ def TileReductionUsingScfOp : Op<Transform_Dialect, "structured.tile_reduction_u
   }];
 
   // TODO: support mixed static-dynamic (see TileToForallOp).
-  let arguments = (ins PDL_Operation:$target,
+  let arguments = (ins TransformHandleTypeInterface:$target,
                    DefaultValuedAttr<DenseI64ArrayAttr, "{}">:$tile_sizes);
-  let results = (outs PDL_Operation:$for_op,
-                      PDL_Operation:$fill_op,
-                      PDL_Operation:$split_linalg_op,
-                      PDL_Operation:$combining_linalg_op);
+  let results = (outs TransformHandleTypeInterface:$for_op,
+                      TransformHandleTypeInterface:$fill_op,
+                      TransformHandleTypeInterface:$split_linalg_op,
+                      TransformHandleTypeInterface:$combining_linalg_op);
 
   let builders = [
     OpBuilder<(ins "Value":$target,
@@ -1342,6 +1358,7 @@ def TileReductionUsingScfOp : Op<Transform_Dialect, "structured.tile_reduction_u
     $target
     `by` `tile_sizes` `=` $tile_sizes
     attr-dict
+    `:` functional-type(operands, results)
   }];
 
   let extraClassDeclaration = [{
@@ -1427,14 +1444,14 @@ def TileReductionUsingForallOp :
   }];
 
   // TODO: support mixed static-dynamic (see TileToForallOp).
-  let arguments = (ins PDL_Operation:$target,
+  let arguments = (ins TransformHandleTypeInterface:$target,
                    DefaultValuedAttr<DenseI64ArrayAttr, "{}">:$num_threads,
                    DefaultValuedAttr<DenseI64ArrayAttr, "{}">:$tile_sizes,
                    OptionalAttr<DeviceMappingArrayAttr>:$mapping);
-  let results = (outs PDL_Operation:$forall_op,
-                      PDL_Operation:$fill_op,
-                      PDL_Operation:$split_linalg_op,
-                      PDL_Operation:$combining_linalg_op);
+  let results = (outs TransformHandleTypeInterface:$forall_op,
+                      TransformHandleTypeInterface:$fill_op,
+                      TransformHandleTypeInterface:$split_linalg_op,
+                      TransformHandleTypeInterface:$combining_linalg_op);
 
   let builders = [
     OpBuilder<(ins "Value":$target,
@@ -1450,6 +1467,7 @@ def TileReductionUsingForallOp :
     (`,` `tile_sizes` `=` $tile_sizes^)?
     (`,` `mapping` `=` $mapping^)?
     attr-dict
+    `:` functional-type(operands, results)
   }];
 
   let extraClassDeclaration = [{
@@ -1577,7 +1595,7 @@ def TileToForallOp :
     This operation ignores ops that do not implement the TilingInterface and
     drops them in the return.
 
-    If all the operations referred to by the `target` PDLOperation tile
+    If all the operations referred to by the `target` handle tile
     successfully, the transform succeeds.
     Otherwise the transform silently fails.
 
@@ -1604,16 +1622,16 @@ def TileToForallOp :
     ```
   }];
 
-  let arguments = (ins PDL_Operation:$target,
-                   Variadic<PDL_Operation>:$num_threads,
-                   Variadic<PDL_Operation>:$tile_sizes,
-                   Optional<PDL_Operation>:$packed_num_threads,
-                   Optional<PDL_Operation>:$packed_tile_sizes,
+  let arguments = (ins TransformHandleTypeInterface:$target,
+                   Variadic<TransformHandleTypeInterface>:$num_threads,
+                   Variadic<TransformHandleTypeInterface>:$tile_sizes,
+                   Optional<TransformHandleTypeInterface>:$packed_num_threads,
+                   Optional<TransformHandleTypeInterface>:$packed_tile_sizes,
                    DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$static_num_threads,
                    DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$static_tile_sizes,
                    OptionalAttr<DeviceMappingArrayAttr>:$mapping);
-  let results = (outs PDL_Operation:$forall_op,
-                      PDL_Operation:$tiled_op);
+  let results = (outs TransformHandleTypeInterface:$forall_op,
+                      TransformHandleTypeInterface:$tiled_op);
 
   let builders = [
     OpBuilder<(ins "Value":$target,
@@ -1641,12 +1659,17 @@ def TileToForallOp :
   let assemblyFormat = [{
     $target oilist(
         `num_threads` custom<PackedOrDynamicIndexList>($packed_num_threads,
+                                                       type($packed_num_threads),
                                                        $num_threads,
+                                                       type($num_threads),
                                                        $static_num_threads) |
          `tile_sizes` custom<PackedOrDynamicIndexList>($packed_tile_sizes,
+                                                       type($packed_tile_sizes),
                                                        $tile_sizes,
+                                                       type($tile_sizes),
                                                        $static_tile_sizes))
     (`(` `mapping` `=` $mapping^ `)`)? attr-dict
+    `:` functional-type($target, results)
   }];
   let hasVerifier = 1;
 
@@ -1705,12 +1728,12 @@ def TileToScfForOp : Op<Transform_Dialect, "structured.tile_to_scf_for",
     produces a definite failure.
   }];
 
-  let arguments = (ins PDL_Operation:$target,
-                   Variadic<PDL_Operation>:$dynamic_sizes,
+  let arguments = (ins TransformHandleTypeInterface:$target,
+                   Variadic<TransformHandleTypeInterface>:$dynamic_sizes,
                    DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$static_sizes,
                    DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:$interchange);
-  let results = (outs PDL_Operation:$tiled_linalg_op,
-                      Variadic<PDL_Operation>:$loops);
+  let results = (outs TransformHandleTypeInterface:$tiled_linalg_op,
+                      Variadic<TransformHandleTypeInterface>:$loops);
   
   let builders = [
     OpBuilder<(ins "Value":$target,
@@ -1760,7 +1783,7 @@ def VectorizeOp : Op<Transform_Dialect, "structured.vectorize",
       - `disable_transfer_permutation_map_lowering_patterns`: a UnitAttr to
       deactivate the rewrite of `vector.transfer` with permutation maps into
       explicit `vector.transpose` operations. This is intended to be used in
-      tests only but may be promotoed to a first class attribute in the future.
+      tests only but may be promoted to a first class attribute in the future.
 
     #### Return modes:
 
@@ -1770,14 +1793,16 @@ def VectorizeOp : Op<Transform_Dialect, "structured.vectorize",
     to be isolated from above.
   }];
 
-  let arguments = (ins PDL_Operation:$target,
+  let arguments = (ins TransformHandleTypeInterface:$target,
                    UnitAttr:$vectorize_padding,
                    UnitAttr:$vectorize_nd_extract,
                    UnitAttr:$disable_multi_reduction_to_contract_patterns,
                    UnitAttr:$disable_transfer_permutation_map_lowering_patterns);
-  let results = (outs PDL_Operation:$transformed);
+  let results = (outs TransformHandleTypeInterface:$transformed);
 
-  let assemblyFormat = "$target attr-dict";
+  let assemblyFormat = 
+      "$target attr-dict `:`"
+      "functional-type(operands, results)";
 
   let builders = [
     OpBuilder<(ins "Value":$target,
@@ -1812,13 +1837,13 @@ def MaskedVectorizeOp : Op<Transform_Dialect, "structured.masked_vectorize",
     #### Return modes:
 
     This operation produces a definite failure if the dynamic vector sizes (SSA
-    values) do not satify the constraints mentioned above. It produces a
+    values) do not satisfy the constraints mentioned above. It produces a
     silenceable failure if at least one target op is not a Linalg op or fails to
     vectorize.
   }];
 
-  let arguments = (ins PDL_Operation:$target,
-                       Variadic<PDL_Operation>:$vector_sizes,
+  let arguments = (ins TransformHandleTypeInterface:$target,
+                       Variadic<TransformHandleTypeInterface>:$vector_sizes,
                        UnitAttr:$vectorize_nd_extract,
                        DefaultValuedOptionalAttr<DenseI64ArrayAttr, "{}">:
                           $static_vector_sizes);
@@ -1826,8 +1851,10 @@ def MaskedVectorizeOp : Op<Transform_Dialect, "structured.masked_vectorize",
   let assemblyFormat = [{
       $target
       `vector_sizes` custom<DynamicIndexList>($vector_sizes,
-                                              $static_vector_sizes)
+                                              $static_vector_sizes,
+                                              type($vector_sizes))
       attr-dict
+      `:` type($target)
   }];
 
   let extraClassDeclaration = [{
diff --git a/mlir/include/mlir/Dialect/Linalg/TransformOps/Syntax.h b/mlir/include/mlir/Dialect/Linalg/TransformOps/Syntax.h
new file mode 100644
index 000000000000..13b0dc0e0b95
--- /dev/null
+++ b/mlir/include/mlir/Dialect/Linalg/TransformOps/Syntax.h
@@ -0,0 +1,47 @@
+//===- Syntax.h - Custom syntax for Linalg transform ops --------*- C++ -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_DIALECT_LINALG_TRANSFORMOPS_SYNTAX_H
+#define MLIR_DIALECT_LINALG_TRANSFORMOPS_SYNTAX_H
+
+#include "mlir/Support/LLVM.h"
+
+namespace mlir {
+class ParseResult;
+class OpAsmParser;
+class OpAsmPrinter;
+class Type;
+class TypeRange;
+class Operation;
+
+/// Parses a single non-function type or a function type with at least one
+/// argument. This allows for the following syntax:
+///
+///   - type: just the argument type;
+///   - `(` type `)` `->` type: one argument and one result type;
+///   - `(` type `)` `->` `(` comma-separated-type-list `)`: one argument and
+///     multiple result types.
+///
+/// Unlike FunctionType, this allows and requires one to omit the parens around
+/// the argument type in absence of result types, and does not accept the
+/// trailing `-> ()` construct, which makes the syntax nicer for operations.
+ParseResult parseSemiFunctionType(OpAsmParser &parser, Type &argumentType,
+                                  Type &resultType);
+ParseResult parseSemiFunctionType(OpAsmParser &parser, Type &argumentType,
+                                  SmallVectorImpl<Type> &resultTypes);
+
+/// Prints argument and result types in a syntax similar to that of FunctionType
+/// but allowing and requiring one to omit the parens around the argument type
+/// in absence of result types, and without the trailing `-> ()`.
+void printSemiFunctionType(OpAsmPrinter &printer, Operation *op,
+                           Type argumentType, TypeRange resultType);
+void printSemiFunctionType(OpAsmPrinter &printer, Operation *op,
+                           Type argumentType, Type resultType);
+} // namespace mlir
+
+#endif // MLIR_DIALECT_LINALG_TRANSFORMOPS_SYNTAX_H
diff --git a/mlir/include/mlir/Dialect/Transform/Utils/Utils.h b/mlir/include/mlir/Dialect/Transform/Utils/Utils.h
index 04a0b090e6b9..97b193bc723d 100644
--- a/mlir/include/mlir/Dialect/Transform/Utils/Utils.h
+++ b/mlir/include/mlir/Dialect/Transform/Utils/Utils.h
@@ -22,7 +22,8 @@ class TransformState;
 
 /// Printer hook for custom directive in assemblyFormat.
 ///
-///   custom<PackedOrDynamicIndexList>($packed, $values, $integers)
+///   custom<PackedOrDynamicIndexList>($packed, type($packed), $values,
+///       type($values), $integers)
 ///
 /// where `values` are variadic Index values, `integers` is an `I64ArrayAttr`
 /// and `packed` is a single transform dialect handle who's mapped payload ops
@@ -30,20 +31,23 @@ class TransformState;
 /// or the other two parameters may be specified.
 ///
 /// This allows idiomatic printing of mixed value and integer attributes in a
-/// list or with a single handle. E.g., `[%arg0, 7, 42, %arg42]` or just `%h`.
+/// list or with a single handle. E.g., `[%arg0 : !transform.any_op, 7, 42,
+/// %arg42 : !transform.param<i64>]` or just `%h : !transform.any_op`.
 void printPackedOrDynamicIndexList(OpAsmPrinter &printer, Operation *op,
-                                   Value packed, OperandRange values,
+                                   Value packed, Type packedType,
+                                   OperandRange values, TypeRange valueTypes,
                                    ArrayRef<int64_t> integers);
 
-/// Pasrer hook for custom directive in assemblyFormat.
+/// Parser hook for custom directive in assemblyFormat.
 ///
-///   custom<PackedOrDynamicIndexList>($packed, $values, $integers)
+///   custom<PackedOrDynamicIndexList>($packed, type($packed), $values,
+///       type($values), $integers)
 ///
 /// See `printPackedOrDynamicIndexList` for details.
 ParseResult parsePackedOrDynamicIndexList(
     OpAsmParser &parser, std::optional<OpAsmParser::UnresolvedOperand> &packed,
-    SmallVectorImpl<OpAsmParser::UnresolvedOperand> &values,
-    DenseI64ArrayAttr &integers);
+    Type &packedType, SmallVectorImpl<OpAsmParser::UnresolvedOperand> &values,
+    SmallVectorImpl<Type> &valueTypes, DenseI64ArrayAttr &integers);
 } // namespace transform
 } // namespace mlir
 
diff --git a/mlir/include/mlir/Interfaces/ViewLikeInterface.h b/mlir/include/mlir/Interfaces/ViewLikeInterface.h
index 5843ecd061df..87113197524f 100644
--- a/mlir/include/mlir/Interfaces/ViewLikeInterface.h
+++ b/mlir/include/mlir/Interfaces/ViewLikeInterface.h
@@ -42,35 +42,49 @@ namespace mlir {
 /// Printer hook for custom directive in assemblyFormat.
 ///
 ///   custom<DynamicIndexList>($values, $integers)
+///   custom<DynamicIndexList>($values, $integers, type($values))
 ///
-/// where `values` is of ODS type `Variadic<Index>` and `integers` is of ODS
+/// where `values` is of ODS type `Variadic<*>` and `integers` is of ODS
 /// type `I64ArrayAttr`. Prints a list with either (1) the static integer value
-/// in `integers` is `dynVal` or (2) the next value otherwise. This allows
-/// idiomatic printing of mixed value and integer attributes in a list. E.g.
-/// `[%arg0, 7, 42, %arg42]`.
+/// in `integers` is `kDynamic` or (2) the next value otherwise. If `valueTypes`
+/// is non-empty, it is expected to contain as many elements as `values`
+/// indicating their types. This allows idiomatic printing of mixed value and
+/// integer attributes in a list. E.g.
+/// `[%arg0 : index, 7, 42, %arg42 : i32]`.
 void printDynamicIndexList(
     OpAsmPrinter &printer, Operation *op, OperandRange values,
-    ArrayRef<int64_t> integers,
+    ArrayRef<int64_t> integers, TypeRange valueTypes = TypeRange(),
     AsmParser::Delimiter delimiter = AsmParser::Delimiter::Square);
 
-/// Pasrer hook for custom directive in assemblyFormat.
+/// Parser hook for custom directive in assemblyFormat.
 ///
 ///   custom<DynamicIndexList>($values, $integers)
+///   custom<DynamicIndexList>($values, $integers, type($values))
 ///
-/// where `values` is of ODS type `Variadic<Index>` and `integers` is of ODS
+/// where `values` is of ODS type `Variadic<*>` and `integers` is of ODS
 /// type `I64ArrayAttr`. Parse a mixed list with either (1) static integer
 /// values or (2) SSA values. Fill `integers` with the integer ArrayAttr, where
-/// `dynVal` encodes the position of SSA values. Add the parsed SSA values
-/// to `values` in-order.
-//
-/// E.g. after parsing "[%arg0, 7, 42, %arg42]":
-///   1. `result` is filled with the i64 ArrayAttr "[`dynVal`, 7, 42, `dynVal`]"
+/// `kDynamic` encodes the position of SSA values. Add the parsed SSA values
+/// to `values` in-order. If `valueTypes` is non-null, fill it with types
+/// corresponding to values; otherwise the caller must handle the types.
+///
+/// E.g. after parsing "[%arg0 : index, 7, 42, %arg42 : i32]":
+///   1. `result` is filled with the i64 ArrayAttr "[`kDynamic`, 7, 42,
+///   `kDynamic`]"
 ///   2. `ssa` is filled with "[%arg0, %arg1]".
 ParseResult parseDynamicIndexList(
     OpAsmParser &parser,
     SmallVectorImpl<OpAsmParser::UnresolvedOperand> &values,
-    DenseI64ArrayAttr &integers,
+    DenseI64ArrayAttr &integers, SmallVectorImpl<Type> *valueTypes = nullptr,
     AsmParser::Delimiter delimiter = AsmParser::Delimiter::Square);
+inline ParseResult parseDynamicIndexList(
+    OpAsmParser &parser,
+    SmallVectorImpl<OpAsmParser::UnresolvedOperand> &values,
+    DenseI64ArrayAttr &integers, SmallVectorImpl<Type> &valueTypes,
+    AsmParser::Delimiter delimiter = AsmParser::Delimiter::Square) {
+  return parseDynamicIndexList(parser, values, integers, &valueTypes,
+                               delimiter);
+}
 
 /// Verify that a the `values` has as many elements as the number of entries in
 /// `attr` for which `isDynamic` evaluates to true.
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/CMakeLists.txt b/mlir/lib/Dialect/Linalg/TransformOps/CMakeLists.txt
index 01038ed297f1..ec1631a9efde 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/CMakeLists.txt
+++ b/mlir/lib/Dialect/Linalg/TransformOps/CMakeLists.txt
@@ -2,6 +2,7 @@ add_mlir_dialect_library(MLIRLinalgTransformOps
   DialectExtension.cpp
   LinalgMatchOps.cpp
   LinalgTransformOps.cpp
+  Syntax.cpp
 
   ADDITIONAL_HEADER_DIRS
   ${MLIR_MAIN_INCLUDE_DIR}/mlir/Dialect/Linalg/TransformOps
@@ -19,7 +20,6 @@ add_mlir_dialect_library(MLIRLinalgTransformOps
   MLIRLinalgDialect
   MLIRLinalgTransforms
   MLIRParser
-  MLIRPDLDialect
   MLIRSCFDialect
   MLIRSideEffectInterfaces
   MLIRTransformDialect
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/DialectExtension.cpp b/mlir/lib/Dialect/Linalg/TransformOps/DialectExtension.cpp
index 6cc296105be7..9578648d6363 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/DialectExtension.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/DialectExtension.cpp
@@ -13,7 +13,6 @@
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
 #include "mlir/Dialect/Linalg/TransformOps/LinalgMatchOps.h"
 #include "mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.h"
-#include "mlir/Dialect/PDL/IR/PDL.h"
 #include "mlir/Dialect/SCF/IR/SCF.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Transform/IR/TransformDialect.h"
@@ -31,7 +30,6 @@ public:
   using Base::Base;
 
   void init() {
-    declareDependentDialect<pdl::PDLDialect>();
     declareDependentDialect<linalg::LinalgDialect>();
 
     declareGeneratedDialect<affine::AffineDialect>();
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgMatchOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgMatchOps.cpp
index 0d9d533da366..d788c2feaac8 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgMatchOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgMatchOps.cpp
@@ -9,6 +9,7 @@
 #include "mlir/Dialect/Linalg/TransformOps/LinalgMatchOps.h"
 #include "mlir/Analysis/SliceAnalysis.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
+#include "mlir/Dialect/Linalg/TransformOps/Syntax.h"
 #include "mlir/Dialect/Transform/IR/MatchInterfaces.h"
 #include "mlir/IR/BuiltinAttributes.h"
 #include "mlir/IR/FunctionImplementation.h"
@@ -745,82 +746,6 @@ static void printStructuredTransformDims(OpAsmPrinter &printer, Operation *op,
     printer << ")";
   }
 }
-/// Parses a single non-function type or a function type with at least one
-/// argument. This allows for the following syntax:
-///
-///   - type: just the argument type;
-///   - `(` type `)` `->` type: one argument and one result type;
-///   - `(` type `)` `->` `(` comma-separated-type-list `)`: one argument and
-///     multiple result types.
-///
-/// Unlike FunctionType, this allows and requires one to omit the parens around
-/// the argument type in absence of result types, and does not accept the
-/// trailing `-> ()` construct, which makes the syntax nicer for operations.
-static ParseResult parseSemiFunctionType(OpAsmParser &parser,
-                                         Type &argumentType, Type &resultType) {
-  argumentType = resultType = nullptr;
-  bool hasLParen = parser.parseOptionalLParen().succeeded();
-  if (parser.parseType(argumentType).failed())
-    return failure();
-  if (!hasLParen)
-    return success();
-
-  return failure(parser.parseRParen().failed() ||
-                 parser.parseArrow().failed() ||
-                 parser.parseType(resultType).failed());
-}
-static ParseResult parseSemiFunctionType(OpAsmParser &parser,
-                                         Type &argumentType,
-                                         SmallVectorImpl<Type> &resultTypes) {
-  argumentType = nullptr;
-  bool hasLParen = parser.parseOptionalLParen().succeeded();
-  if (parser.parseType(argumentType).failed())
-    return failure();
-  if (!hasLParen)
-    return success();
-
-  if (parser.parseRParen().failed() || parser.parseArrow().failed())
-    return failure();
-
-  if (parser.parseOptionalLParen().failed()) {
-    Type type;
-    if (parser.parseType(type).failed())
-      return failure();
-    resultTypes.push_back(type);
-    return success();
-  }
-  if (parser.parseTypeList(resultTypes).failed() ||
-      parser.parseRParen().failed()) {
-    resultTypes.clear();
-    return failure();
-  }
-  return success();
-}
-
-/// Prints argument and result types in a syntax similar to that of FunctionType
-/// but allowing and requiring one to omit the parens around the argument type
-/// in absence of result types, and without the trailing `-> ()`.
-static void printSemiFunctionType(OpAsmPrinter &printer, Operation *op,
-                                  Type argumentType, TypeRange resultType) {
-  if (!resultType.empty())
-    printer << "(";
-  printer << argumentType;
-  if (resultType.empty())
-    return;
-  printer << ") -> ";
-
-  if (resultType.size() > 1)
-    printer << "(";
-  llvm::interleaveComma(resultType, printer.getStream());
-  if (resultType.size() > 1)
-    printer << ")";
-}
-static void printSemiFunctionType(OpAsmPrinter &printer, Operation *op,
-                                  Type argumentType, Type resultType) {
-  return printSemiFunctionType(printer, op, argumentType,
-                               resultType ? TypeRange(resultType)
-                                          : TypeRange());
-}
 
 #define GET_OP_CLASSES
 #include "mlir/Dialect/Linalg/TransformOps/LinalgMatchOps.cpp.inc"
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
index 0703ca31f402..baabf1ae67fc 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
@@ -14,11 +14,10 @@
 #include "mlir/Dialect/Arith/IR/Arith.h"
 #include "mlir/Dialect/GPU/IR/GPUDialect.h"
 #include "mlir/Dialect/Linalg/IR/Linalg.h"
+#include "mlir/Dialect/Linalg/TransformOps/Syntax.h"
 #include "mlir/Dialect/Linalg/Transforms/Hoisting.h"
 #include "mlir/Dialect/Linalg/Transforms/Transforms.h"
 #include "mlir/Dialect/Linalg/Utils/Utils.h"
-#include "mlir/Dialect/PDL/IR/PDL.h"
-#include "mlir/Dialect/PDL/IR/PDLTypes.h"
 #include "mlir/Dialect/SCF/Transforms/TileUsingInterface.h"
 #include "mlir/Dialect/Tensor/IR/Tensor.h"
 #include "mlir/Dialect/Tensor/Utils/Utils.h"
@@ -82,7 +81,7 @@ static FailureOr<LinalgOp> tryApply(Operation *operation, Args &&...args) {
 
 /// Assuming that `ofr` is an index attr or a transform dialect handle mapped
 /// to exactly one op with one index result, return that value.
-static DiagnosedSilenceableFailure unpackSingleIndexResultPDLOperations(
+static DiagnosedSilenceableFailure unpackSingleIndexResultPayloadOperations(
     transform::TransformState &state, TransformOpInterface transformOp,
     SmallVector<OpFoldResult> &result, ArrayRef<OpFoldResult> ofrs) {
   for (OpFoldResult ofr : ofrs) {
@@ -122,7 +121,7 @@ static DiagnosedSilenceableFailure unpackSingleIndexResultPDLOperations(
 // replaced with the first (and only) OpResult of that payload op. (There
 // must be exactly one mapped payload op and it must have exactly one
 // index result.)
-static DiagnosedSilenceableFailure unpackSingleIndexResultPDLOperations(
+static DiagnosedSilenceableFailure unpackSingleIndexResultPayloadOperations(
     transform::TransformState &state, TransformOpInterface transformOp,
     SmallVector<OpFoldResult> &result, Value packedHandle) {
   for (Operation *op : state.getPayloadOps(packedHandle)) {
@@ -259,34 +258,6 @@ static LogicalResult applyTilingToAll(
   return success();
 }
 
-/// Parse a tiling-like operation that returns the tiled op as well as the
-/// created tile loops. The function counts the non-zero tile sizes to compute
-/// the number of results.
-static ParseResult parseTileLikeOp(OpAsmParser &parser, OperationState &result,
-                                   StringRef sizesAttrName) {
-  OpAsmParser::UnresolvedOperand targetOperand;
-  SMLoc opLoc = parser.getCurrentLocation();
-  if (parser.parseOperand(targetOperand) ||
-      parser.parseOptionalAttrDict(result.attributes))
-    return failure();
-  Attribute sizesAttr = result.attributes.get(sizesAttrName);
-  if (!sizesAttr)
-    return parser.emitError(opLoc)
-           << "expected '" << sizesAttrName << "' attribute";
-  auto sizesArrayAttr = dyn_cast<ArrayAttr>(sizesAttr);
-  if (!sizesArrayAttr)
-    return parser.emitError(opLoc)
-           << "'" << sizesAttrName << "' attribute must be an array";
-  Type pdlOpType = parser.getBuilder().getType<pdl::OperationType>();
-  size_t numExpectedLoops =
-      sizesArrayAttr.size() -
-      llvm::count(extractFromI64ArrayAttr(sizesArrayAttr), 0);
-  result.addTypes(SmallVector<Type>(numExpectedLoops + 1, pdlOpType));
-  if (parser.resolveOperand(targetOperand, pdlOpType, result.operands))
-    return failure();
-  return success();
-}
-
 DiagnosedSilenceableFailure
 transform::FuseOp::apply(mlir::transform::TransformResults &transformResults,
                          mlir::transform::TransformState &state) {
@@ -315,15 +286,34 @@ transform::FuseOp::apply(mlir::transform::TransformResults &transformResults,
 
 ParseResult transform::FuseOp::parse(OpAsmParser &parser,
                                      OperationState &result) {
-  return parseTileLikeOp(
-      parser, result,
-      transform::FuseOp::getTileSizesAttrName(result.name).getValue());
+  OpAsmParser::UnresolvedOperand targetOperand;
+  if (parser.parseOperand(targetOperand) ||
+      parser.parseOptionalAttrDict(result.attributes))
+    return failure();
+
+  FunctionType trailingType;
+  SMLoc typeLoc;
+  if (parser.getCurrentLocation(&typeLoc) ||
+      parser.parseColonType(trailingType)) {
+    return failure();
+  }
+  if (trailingType.getNumInputs() != 1)
+    return parser.emitError(typeLoc) << "expected one input type";
+
+  result.addTypes(trailingType.getResults());
+  if (parser.resolveOperand(targetOperand, trailingType.getInput(0),
+                            result.operands))
+    return failure();
+  return success();
 }
 
 void transform::FuseOp::print(OpAsmPrinter &p) {
   p << ' ';
   p << getTarget();
   p.printOptionalAttrDict((*this)->getAttrs());
+  p << " : ";
+  p.printFunctionalType(TypeRange(getOperand().getType()),
+                        getResults().getTypes());
 }
 
 LogicalResult transform::FuseOp::verify() {
@@ -335,6 +325,12 @@ LogicalResult transform::FuseOp::verify() {
     return emitOpError() << "expects interchange to be a permutation, found "
                          << getTileInterchange();
   }
+
+  SmallVector<int64_t> sizes = extractFromI64ArrayAttr(getTileSizes());
+  size_t numExpectedLoops = sizes.size() - llvm::count(sizes, 0);
+  if (numExpectedLoops != getNumResults() - 1)
+    return emitOpError() << "expects " << numExpectedLoops << " loop results";
+
   return success();
 }
 
@@ -347,7 +343,7 @@ void transform::FuseIntoContainingOp::build(OpBuilder &builder,
                                             Value producerOp,
                                             Value containingOp) {
   result.addOperands({producerOp, containingOp});
-  result.addTypes(pdl::OperationType::get(builder.getContext()));
+  result.addTypes(transform::AnyOpType::get(builder.getContext()));
 }
 
 /// Find the first "extract" user of `producerOp` and tile it right before its
@@ -792,7 +788,7 @@ void transform::MatchOp::build(OpBuilder &builder, OperationState &result,
   result.addOperands(target);
   result.addAttribute(MatchOp::getOpsAttrName(result.name),
                       builder.getStrArrayAttr(opNames));
-  result.addTypes(pdl::OperationType::get(builder.getContext()));
+  result.addTypes(transform::AnyOpType::get(builder.getContext()));
 }
 
 void transform::MatchOp::build(OpBuilder &builder, OperationState &result,
@@ -1022,7 +1018,7 @@ transform::PackOp::apply(transform::TransformResults &transformResults,
 
   // Unpack handles to constants or actual SSA index values.
   SmallVector<OpFoldResult> packedSizes;
-  DiagnosedSilenceableFailure status = unpackSingleIndexResultPDLOperations(
+  DiagnosedSilenceableFailure status = unpackSingleIndexResultPayloadOperations(
       state, *this, packedSizes, getMixedPackedSizes());
 
   TrackingListener listener(state, *this);
@@ -2011,7 +2007,7 @@ void transform::SplitReductionOp::build(
     result.addAttribute(SplitReductionOp::getUseAllocAttrName(result.name),
                         builder.getUnitAttr());
   }
-  auto resultType = pdl::OperationType::get(ctx);
+  auto resultType = transform::AnyOpType::get(ctx);
   result.addTypes({resultType, resultType, resultType, resultType});
 }
 
@@ -2053,7 +2049,7 @@ void transform::TileReductionUsingScfOp::build(
   // In the absence of this, horrible bugs ensue.
   // TODO: support mixed static-dynamic (see TileToForallOp).
   MLIRContext *ctx = builder.getContext();
-  auto opTy = pdl::OperationType::get(ctx);
+  auto opTy = transform::AnyOpType::get(ctx);
   auto staticTileSizesAttr = builder.getDenseI64ArrayAttr(staticTileSizes);
   build(builder, result,
         /*resultTypes=*/TypeRange{opTy, opTy, opTy, opTy},
@@ -2094,7 +2090,7 @@ void transform::TileReductionUsingForallOp::build(
   // In the absence of this, horrible bugs ensue.
   // TODO: support mixed static-dynamic (see TileToForallOp).
   MLIRContext *ctx = builder.getContext();
-  auto opTy = pdl::OperationType::get(ctx);
+  auto opTy = transform::AnyOpType::get(ctx);
   auto staticNumThreadsAttr = builder.getDenseI64ArrayAttr(staticNumThreads);
   auto staticTileSizesAttr = builder.getDenseI64ArrayAttr(staticTileSizes);
   build(builder, result,
@@ -2448,7 +2444,7 @@ void transform::TileToForallOp::build(OpBuilder &builder,
   // attributes for multiple variadic operands. In the absence of this,
   // horrible bugs ensue.
   MLIRContext *ctx = builder.getContext();
-  auto operationType = pdl::OperationType::get(ctx);
+  auto operationType = transform::AnyOpType::get(ctx);
   auto staticTileSizesAttr = builder.getDenseI64ArrayAttr(staticTileSizes);
   build(builder, result,
         /*resultTypes=*/TypeRange{operationType, operationType},
@@ -2485,7 +2481,7 @@ void transform::TileToForallOp::build(OpBuilder &builder,
   // attributes for multiple variadic operands. In the absence of this,
   // horrible bugs ensue.
   MLIRContext *ctx = builder.getContext();
-  auto operationType = pdl::OperationType::get(ctx);
+  auto operationType = transform::AnyOpType::get(ctx);
   auto staticNumThreadsAttr = builder.getDenseI64ArrayAttr(staticNumThreads);
   build(builder, result,
         /*resultTypes=*/TypeRange{operationType, operationType},
@@ -2547,17 +2543,17 @@ transform::TileToForallOp::apply(transform::TransformResults &transformResults,
   SmallVector<OpFoldResult> mixedNumThreads;
   DiagnosedSilenceableFailure status =
       getPackedNumThreads()
-          ? unpackSingleIndexResultPDLOperations(
+          ? unpackSingleIndexResultPayloadOperations(
                 state, transformOp, mixedNumThreads, getPackedNumThreads())
-          : unpackSingleIndexResultPDLOperations(
+          : unpackSingleIndexResultPayloadOperations(
                 state, transformOp, mixedNumThreads, getMixedNumThreads());
   if (!status.succeeded())
     return status;
   SmallVector<OpFoldResult> mixedTileSizes;
   status = getPackedTileSizes()
-               ? unpackSingleIndexResultPDLOperations(
+               ? unpackSingleIndexResultPayloadOperations(
                      state, transformOp, mixedTileSizes, getPackedTileSizes())
-               : unpackSingleIndexResultPDLOperations(
+               : unpackSingleIndexResultPayloadOperations(
                      state, transformOp, mixedTileSizes, getMixedTileSizes());
   if (!status.succeeded())
     return status;
@@ -2634,8 +2630,8 @@ void transform::TileToScfForOp::build(OpBuilder &builder,
   auto staticTileSizesAttr = builder.getDenseI64ArrayAttr(staticTileSizes);
   int64_t numExpectedLoops =
       staticTileSizes.size() - llvm::count(staticTileSizes, 0);
-  SmallVector<Type> resultTypes(numExpectedLoops,
-                                pdl::OperationType::get(builder.getContext()));
+  SmallVector<Type> resultTypes(
+      numExpectedLoops, transform::AnyOpType::get(builder.getContext()));
   build(builder, result,
         /*tiled_linalg_op=*/target.getType(),
         /*loops=*/resultTypes,
@@ -2758,20 +2754,43 @@ ParseResult transform::TileToScfForOp::parse(OpAsmParser &parser,
   OpAsmParser::UnresolvedOperand target;
   SmallVector<OpAsmParser::UnresolvedOperand> dynamicSizes;
   DenseI64ArrayAttr staticSizes;
-  auto pdlOperationType = pdl::OperationType::get(parser.getContext());
+  FunctionType trailingType;
+  llvm::SMLoc typeLoc;
   if (parser.parseOperand(target) ||
-      parser.resolveOperand(target, pdlOperationType, result.operands) ||
       parseDynamicIndexList(parser, dynamicSizes, staticSizes) ||
-      parser.resolveOperands(dynamicSizes, pdlOperationType, result.operands))
+      parseOptionalInterchange(parser, result) ||
+      parser.parseOptionalAttrDict(result.attributes) ||
+      parser.getCurrentLocation(&typeLoc) ||
+      parser.parseColonType(trailingType)) {
     return ParseResult::failure();
+  }
 
-  // Parse optional interchange.
-  if (failed(parseOptionalInterchange(parser, result)))
-    return ParseResult::failure();
   result.addAttribute(getStaticSizesAttrName(result.name), staticSizes);
   size_t numExpectedLoops =
       staticSizes.size() - llvm::count(staticSizes.asArrayRef(), 0);
-  result.addTypes(SmallVector<Type>(numExpectedLoops + 1, pdlOperationType));
+
+  unsigned numExpectedInputTypes = 1 + dynamicSizes.size();
+  if (trailingType.getNumInputs() != numExpectedInputTypes) {
+    return parser.emitError(typeLoc)
+           << "expected " << numExpectedInputTypes << " operand types, got "
+           << trailingType.getNumInputs();
+  }
+
+  unsigned numExpectedOutputTypes = 1 + numExpectedLoops;
+  if (trailingType.getNumResults() != numExpectedOutputTypes) {
+    return parser.emitError(typeLoc)
+           << "expected " << numExpectedOutputTypes << " result types, got "
+           << trailingType.getNumResults();
+  }
+
+  if (parser.resolveOperand(target, trailingType.getInput(0),
+                            result.operands) ||
+      parser.resolveOperands(dynamicSizes,
+                             trailingType.getInputs().drop_front(), typeLoc,
+                             result.operands) ||
+      parser.addTypesToList(trailingType.getResults(), result.types)) {
+    return failure();
+  }
   return success();
 }
 
@@ -2779,6 +2798,9 @@ void TileToScfForOp::print(OpAsmPrinter &p) {
   p << ' ' << getTarget();
   printDynamicIndexList(p, getOperation(), getDynamicSizes(), getStaticSizes());
   printOptionalInterchange(p, getInterchange());
+  p.printOptionalAttrDict(getOperation()->getAttrs(), getAttributeNames());
+  p << " : ";
+  p.printFunctionalType(getOperation());
 }
 
 void transform::TileToScfForOp::getEffects(
@@ -2806,7 +2828,7 @@ void transform::VectorizeOp::build(OpBuilder &builder, OperationState &result,
     result.addAttribute(VectorizeOp::getVectorizeNdExtractAttrName(result.name),
                         builder.getUnitAttr());
   }
-  result.addTypes(pdl::OperationType::get(builder.getContext()));
+  result.addTypes(transform::AnyOpType::get(builder.getContext()));
 }
 
 namespace {
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/Syntax.cpp b/mlir/lib/Dialect/Linalg/TransformOps/Syntax.cpp
new file mode 100644
index 000000000000..7ba0a6eb68f4
--- /dev/null
+++ b/mlir/lib/Dialect/Linalg/TransformOps/Syntax.cpp
@@ -0,0 +1,76 @@
+//===- Syntax.cpp - Custom syntax for Linalg transform ops ----------------===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+
+#include "mlir/Dialect/Linalg/TransformOps/Syntax.h"
+#include "mlir/IR/OpImplementation.h"
+
+using namespace mlir;
+
+ParseResult mlir::parseSemiFunctionType(OpAsmParser &parser, Type &argumentType,
+                                        Type &resultType) {
+  argumentType = resultType = nullptr;
+  bool hasLParen = parser.parseOptionalLParen().succeeded();
+  if (parser.parseType(argumentType).failed())
+    return failure();
+  if (!hasLParen)
+    return success();
+
+  return failure(parser.parseRParen().failed() ||
+                 parser.parseArrow().failed() ||
+                 parser.parseType(resultType).failed());
+}
+
+ParseResult mlir::parseSemiFunctionType(OpAsmParser &parser, Type &argumentType,
+                                        SmallVectorImpl<Type> &resultTypes) {
+  argumentType = nullptr;
+  bool hasLParen = parser.parseOptionalLParen().succeeded();
+  if (parser.parseType(argumentType).failed())
+    return failure();
+  if (!hasLParen)
+    return success();
+
+  if (parser.parseRParen().failed() || parser.parseArrow().failed())
+    return failure();
+
+  if (parser.parseOptionalLParen().failed()) {
+    Type type;
+    if (parser.parseType(type).failed())
+      return failure();
+    resultTypes.push_back(type);
+    return success();
+  }
+  if (parser.parseTypeList(resultTypes).failed() ||
+      parser.parseRParen().failed()) {
+    resultTypes.clear();
+    return failure();
+  }
+  return success();
+}
+
+void mlir::printSemiFunctionType(OpAsmPrinter &printer, Operation *op,
+                                 Type argumentType, TypeRange resultType) {
+  if (!resultType.empty())
+    printer << "(";
+  printer << argumentType;
+  if (resultType.empty())
+    return;
+  printer << ") -> ";
+
+  if (resultType.size() > 1)
+    printer << "(";
+  llvm::interleaveComma(resultType, printer.getStream());
+  if (resultType.size() > 1)
+    printer << ")";
+}
+
+void mlir::printSemiFunctionType(OpAsmPrinter &printer, Operation *op,
+                                 Type argumentType, Type resultType) {
+  return printSemiFunctionType(printer, op, argumentType,
+                               resultType ? TypeRange(resultType)
+                                          : TypeRange());
+}
diff --git a/mlir/lib/Dialect/SCF/IR/SCF.cpp b/mlir/lib/Dialect/SCF/IR/SCF.cpp
index a88b9136974a..7048d1348ca8 100644
--- a/mlir/lib/Dialect/SCF/IR/SCF.cpp
+++ b/mlir/lib/Dialect/SCF/IR/SCF.cpp
@@ -1220,17 +1220,17 @@ void ForallOp::print(OpAsmPrinter &p) {
   if (isNormalized()) {
     p << ") in ";
     printDynamicIndexList(p, op, getDynamicUpperBound(), getStaticUpperBound(),
-                          OpAsmParser::Delimiter::Paren);
+                          /*valueTypes=*/{}, OpAsmParser::Delimiter::Paren);
   } else {
     p << ") = ";
     printDynamicIndexList(p, op, getDynamicLowerBound(), getStaticLowerBound(),
-                          OpAsmParser::Delimiter::Paren);
+                          /*valueTypes=*/{}, OpAsmParser::Delimiter::Paren);
     p << " to ";
     printDynamicIndexList(p, op, getDynamicUpperBound(), getStaticUpperBound(),
-                          OpAsmParser::Delimiter::Paren);
+                          /*valueTypes=*/{}, OpAsmParser::Delimiter::Paren);
     p << " step ";
     printDynamicIndexList(p, op, getDynamicStep(), getStaticStep(),
-                          OpAsmParser::Delimiter::Paren);
+                          /*valueTypes=*/{}, OpAsmParser::Delimiter::Paren);
   }
   printInitializationList(p, getRegionOutArgs(), getOutputs(), " shared_outs");
   p << " ";
@@ -1262,6 +1262,7 @@ ParseResult ForallOp::parse(OpAsmParser &parser, OperationState &result) {
   if (succeeded(parser.parseOptionalKeyword("in"))) {
     // Parse upper bounds.
     if (parseDynamicIndexList(parser, dynamicUbs, staticUbs,
+                              /*valueTypes=*/nullptr,
                               OpAsmParser::Delimiter::Paren) ||
         parser.resolveOperands(dynamicUbs, indexType, result.operands))
       return failure();
@@ -1273,6 +1274,7 @@ ParseResult ForallOp::parse(OpAsmParser &parser, OperationState &result) {
     // Parse lower bounds.
     if (parser.parseEqual() ||
         parseDynamicIndexList(parser, dynamicLbs, staticLbs,
+                              /*valueTypes=*/nullptr,
                               OpAsmParser::Delimiter::Paren) ||
 
         parser.resolveOperands(dynamicLbs, indexType, result.operands))
@@ -1281,6 +1283,7 @@ ParseResult ForallOp::parse(OpAsmParser &parser, OperationState &result) {
     // Parse upper bounds.
     if (parser.parseKeyword("to") ||
         parseDynamicIndexList(parser, dynamicUbs, staticUbs,
+                              /*valueTypes=*/nullptr,
                               OpAsmParser::Delimiter::Paren) ||
         parser.resolveOperands(dynamicUbs, indexType, result.operands))
       return failure();
@@ -1288,6 +1291,7 @@ ParseResult ForallOp::parse(OpAsmParser &parser, OperationState &result) {
     // Parse step values.
     if (parser.parseKeyword("step") ||
         parseDynamicIndexList(parser, dynamicSteps, staticSteps,
+                              /*valueTypes=*/nullptr,
                               OpAsmParser::Delimiter::Paren) ||
         parser.resolveOperands(dynamicSteps, indexType, result.operands))
       return failure();
diff --git a/mlir/lib/Dialect/Transform/Utils/Utils.cpp b/mlir/lib/Dialect/Transform/Utils/Utils.cpp
index 8f67a8886fd3..d516a56feed4 100644
--- a/mlir/lib/Dialect/Transform/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Transform/Utils/Utils.cpp
@@ -15,25 +15,32 @@ using namespace mlir;
 using namespace mlir::transform;
 
 void mlir::transform::printPackedOrDynamicIndexList(
-    OpAsmPrinter &printer, Operation *op, Value packed, OperandRange values,
-    ArrayRef<int64_t> integers) {
+    OpAsmPrinter &printer, Operation *op, Value packed, Type packedType,
+    OperandRange values, TypeRange valueTypes, ArrayRef<int64_t> integers) {
   if (packed) {
     assert(values.empty() && integers.empty() && "expected no values/integers");
-    printer << packed;
+    printer << "*(" << packed << " : " << packedType << ")";
     return;
   }
-  printDynamicIndexList(printer, op, values, integers);
+  printDynamicIndexList(printer, op, values, integers, valueTypes);
 }
 
 ParseResult mlir::transform::parsePackedOrDynamicIndexList(
     OpAsmParser &parser, std::optional<OpAsmParser::UnresolvedOperand> &packed,
-    SmallVectorImpl<OpAsmParser::UnresolvedOperand> &values,
-    DenseI64ArrayAttr &integers) {
+    Type &packedType, SmallVectorImpl<OpAsmParser::UnresolvedOperand> &values,
+    SmallVectorImpl<Type> &valueTypes, DenseI64ArrayAttr &integers) {
   OpAsmParser::UnresolvedOperand packedOperand;
-  if (parser.parseOptionalOperand(packedOperand).has_value()) {
+  if (parser.parseOptionalStar().succeeded()) {
+    if (parser.parseLParen().failed() ||
+        parser.parseOperand(packedOperand).failed() ||
+        parser.parseColonType(packedType).failed() ||
+        parser.parseRParen().failed()) {
+      return failure();
+    }
     packed.emplace(packedOperand);
     integers = parser.getBuilder().getDenseI64ArrayAttr({});
     return success();
   }
-  return parseDynamicIndexList(parser, values, integers);
+
+  return parseDynamicIndexList(parser, values, integers, &valueTypes);
 }
diff --git a/mlir/lib/Interfaces/ViewLikeInterface.cpp b/mlir/lib/Interfaces/ViewLikeInterface.cpp
index 0b1ecc9115fd..4f48f0a57c30 100644
--- a/mlir/lib/Interfaces/ViewLikeInterface.cpp
+++ b/mlir/lib/Interfaces/ViewLikeInterface.cpp
@@ -102,6 +102,7 @@ static char getRightDelimiter(AsmParser::Delimiter delimiter) {
 void mlir::printDynamicIndexList(OpAsmPrinter &printer, Operation *op,
                                  OperandRange values,
                                  ArrayRef<int64_t> integers,
+                                 TypeRange valueTypes,
                                  AsmParser::Delimiter delimiter) {
   char leftDelimiter = getLeftDelimiter(delimiter);
   char rightDelimiter = getRightDelimiter(delimiter);
@@ -112,10 +113,14 @@ void mlir::printDynamicIndexList(OpAsmPrinter &printer, Operation *op,
   }
   unsigned idx = 0;
   llvm::interleaveComma(integers, printer, [&](int64_t integer) {
-    if (ShapedType::isDynamic(integer))
-      printer << values[idx++];
-    else
+    if (ShapedType::isDynamic(integer)) {
+      printer << values[idx];
+      if (!valueTypes.empty())
+        printer << " : " << valueTypes[idx];
+      ++idx;
+    } else {
       printer << integer;
+    }
   });
   printer << rightDelimiter;
 }
@@ -123,7 +128,8 @@ void mlir::printDynamicIndexList(OpAsmPrinter &printer, Operation *op,
 ParseResult mlir::parseDynamicIndexList(
     OpAsmParser &parser,
     SmallVectorImpl<OpAsmParser::UnresolvedOperand> &values,
-    DenseI64ArrayAttr &integers, AsmParser::Delimiter delimiter) {
+    DenseI64ArrayAttr &integers, SmallVectorImpl<Type> *valueTypes,
+    AsmParser::Delimiter delimiter) {
 
   SmallVector<int64_t, 4> integerVals;
   auto parseIntegerOrValue = [&]() {
@@ -132,6 +138,8 @@ ParseResult mlir::parseDynamicIndexList(
     if (res.has_value() && succeeded(res.value())) {
       values.push_back(operand);
       integerVals.push_back(ShapedType::kDynamic);
+      if (valueTypes && parser.parseColonType(valueTypes->emplace_back()))
+        return failure();
     } else {
       int64_t integer;
       if (failed(parser.parseInteger(integer)))
diff --git a/mlir/test/Dialect/GPU/transform-gpu-failing.mlir b/mlir/test/Dialect/GPU/transform-gpu-failing.mlir
index 459b800f76d3..813b6f3cd17e 100644
--- a/mlir/test/Dialect/GPU/transform-gpu-failing.mlir
+++ b/mlir/test/Dialect/GPU/transform-gpu-failing.mlir
@@ -133,10 +133,11 @@ func.func @map_nested_forall_to_threads_not_buffer(%x: tensor<32x32xf32>, %y: te
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg0: !pdl.operation):
-  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg0 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg0: !transform.any_op):
+  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg0 : (!transform.any_op) -> !transform.any_op
   %forall, %tiled = transform.structured.tile_to_forall_op %matmul num_threads [10, 20, 30] (mapping = [ #gpu.thread<y>, #gpu.thread<x>, #gpu.thread<z> ] )
-  %funcop = transform.structured.match ops{["gpu.launch"]} in %arg0 : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+  %funcop = transform.structured.match ops{["gpu.launch"]} in %arg0 : (!transform.any_op) -> !pdl.operation
   // expected-error @below {{only bufferized scf.forall can be mapped}}
   transform.gpu.map_nested_forall_to_threads %funcop block_dims = [128, 4, 1]
 }
@@ -298,8 +299,9 @@ func.func @tiling_buffer_semantic_op(%x: memref<32x32xf32>, %y: memref<32x32xf32
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg0: !pdl.operation):
-  %matmul = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg0: !transform.any_op):
+  %matmul = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
   // expected-error @below {{transform.structured.tile_to_forall_op failed to apply}}
   %forall, %tiled = transform.structured.tile_to_forall_op %matmul num_threads [10, 20, 30] (mapping = [ #gpu.thread<y>, #gpu.thread<x>, #gpu.thread<z> ] )
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
diff --git a/mlir/test/Dialect/LLVM/transform-e2e.mlir b/mlir/test/Dialect/LLVM/transform-e2e.mlir
index 0ca820640777..e9025238020b 100644
--- a/mlir/test/Dialect/LLVM/transform-e2e.mlir
+++ b/mlir/test/Dialect/LLVM/transform-e2e.mlir
@@ -13,47 +13,47 @@ func.func @matmul_tensors(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%module_op: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %module_op : (!pdl.operation) -> !pdl.operation
-  %1, %loops:3 = transform.structured.tile %0 [2, 2, 2] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-  %2 = get_closest_isolated_parent %1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.vectorize %2
+^bb1(%module_op: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %module_op : (!transform.any_op) -> !transform.any_op
+  %1, %loops:3 = transform.structured.tile %0 [2, 2, 2] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+  %2 = get_closest_isolated_parent %1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.vectorize %2 : (!transform.any_op) -> !transform.any_op
   %b = transform.bufferization.one_shot_bufferize layout{IdentityLayoutMap}
       %module_op {bufferize_function_boundaries = true}
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %f = transform.structured.match ops{["func.func"]} in %b
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   // TODO: group these lower-level controls into various properly named vector
   // lowering TD macros.
   %func = transform.vector.lower_contraction %f
-    lowering_strategy = "outerproduct" 
-      : (!pdl.operation) -> !pdl.operation
+    lowering_strategy = "outerproduct"
+      : (!transform.any_op) -> !transform.any_op
 
   %func_2 = transform.vector.apply_transfer_permutation_patterns %func
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_3 = transform.vector.lower_multi_reduction %func_2
     lowering_strategy = "innerparallel"
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_4 = transform.vector.split_transfer_full_partial %func_3
     split_transfer_strategy = "linalg-copy"
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_5 = transform.vector.transfer_to_scf %func_4
     max_transfer_rank = 1 full_unroll = true
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_6 = transform.vector.lower_transfer %func_5
     max_transfer_rank = 1
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_7 = transform.vector.lower_shape_cast %func_6
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   %func_8 = transform.vector.lower_transpose %func_7
     lowering_strategy = "shuffle_1d"
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/generalize-tensor-pack-tile.mlir b/mlir/test/Dialect/Linalg/generalize-tensor-pack-tile.mlir
index c22d29027670..ffc0c28ffa8a 100644
--- a/mlir/test/Dialect/Linalg/generalize-tensor-pack-tile.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-tensor-pack-tile.mlir
@@ -25,9 +25,9 @@ func.func @KCRS_to_KCRSsr(%arg0: tensor<1x1x128x64xf32>, %arg1: tensor<1x1x4x8x8
 // CHECK:             %{{.+}} = tensor.insert_slice %[[TRANSP]] into %{{.+}}
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 1, 1]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 1, 1] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -54,9 +54,9 @@ func.func @pad_and_pack(%arg0: tensor<13x15xf32>, %arg1: tensor<2x8x8x2xf32>, %a
 // CHECK:         %{{.+}} = tensor.insert_slice %[[TRANSP]] into %{{.+}}
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [1, 1]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [1, 1] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -87,7 +87,7 @@ func.func @KC_to_CKkc(%arg0: tensor<128x256xf32>, %arg1: tensor<32x4x32x8xf32>)
 // CHECK:             %{{.+}} = tensor.insert_slice %[[SUB_ITER]] into %{{[a-zA-Z0-9]+}}
 // CHECK-SAME:          [%[[C]], %[[K]], 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] : tensor<1x1x32x8xf32> into tensor<32x4x32x8xf32>
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [1, 1]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [1, 1] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
diff --git a/mlir/test/Dialect/Linalg/generalize-tensor-unpack-tile.mlir b/mlir/test/Dialect/Linalg/generalize-tensor-unpack-tile.mlir
index 02b3babc10cc..6e2933daf5e7 100644
--- a/mlir/test/Dialect/Linalg/generalize-tensor-unpack-tile.mlir
+++ b/mlir/test/Dialect/Linalg/generalize-tensor-unpack-tile.mlir
@@ -6,9 +6,9 @@ func.func @KCRSsr_to_KCRS(%arg0: tensor<1x1x4x8x8x32xf32>, %arg1: tensor<1x1x128
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 32, 8]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 32, 8] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 // CHECK-DAG:   #[[MAP0:.+]] = affine_map<(d0) -> (d0 floordiv 32)>
 // CHECK-DAG:   #[[MAP1:.+]] = affine_map<(d0) -> (d0 floordiv 8)>
@@ -68,9 +68,9 @@ func.func @unpack_and_extract_slice(%arg0: tensor<2x8x8x2xf32>, %arg1: tensor<13
 // CHECK-SAME:          [%[[I]], %[[J]]] [%[[OUT_I_SZ]], %[[OUT_J_SZ]]] [1, 1]
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [8, 2]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [8, 2] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -102,7 +102,7 @@ func.func @CKkc_to_KC(%arg0: tensor<32x4x32x8xf32>, %arg1: tensor<128x256xf32>)
 
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [32, 8]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [32, 8] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
diff --git a/mlir/test/Dialect/Linalg/promote.mlir b/mlir/test/Dialect/Linalg/promote.mlir
index b34a86ec901e..4b902acd41f9 100644
--- a/mlir/test/Dialect/Linalg/promote.mlir
+++ b/mlir/test/Dialect/Linalg/promote.mlir
@@ -67,9 +67,9 @@ func.func @matmul_f32(%A: memref<?xi8>, %M: index, %N: index, %K: index) {
 //   CHECK-NOT:         memref.dealloc %[[tmpC]] : memref<24xi8>
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = transform.structured.promote %0 { use_alloca }
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = transform.structured.promote %0 { use_alloca } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -137,9 +137,9 @@ func.func @matmul_f64(%A: memref<?xi8>, %M: index, %N: index, %K: index) {
 //       CHECK:         memref.dealloc %[[tmpC_f64]] : memref<48xi8>
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = transform.structured.promote %0
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = transform.structured.promote %0 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -181,10 +181,10 @@ func.func @gemm_shared(%a : memref<?x?xf32>, %b : memref<?x?xf32>, %c : memref<?
 
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1, %loops:3 = transform.structured.tile %0 [16, 16, 16] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-  %2 = transform.structured.promote %1 { operands_to_promote = [0, 1], mapping = [#gpu.memory_space<workgroup>] }
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1, %loops:3 = transform.structured.tile %0 [16, 16, 16] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+  %2 = transform.structured.promote %1 { operands_to_promote = [0, 1], mapping = [#gpu.memory_space<workgroup>] } : (!transform.any_op) -> !transform.any_op
 }
 
 
@@ -223,10 +223,10 @@ func.func @gemm_private(%a : memref<?x?xf32>, %b : memref<?x?xf32>, %c : memref<
 
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1, %loops:3 = transform.structured.tile %0 [16, 16, 16] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-  %2 = transform.structured.promote %1 { operands_to_promote = [0, 1], mapping = [#gpu.memory_space<private>] }
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1, %loops:3 = transform.structured.tile %0 [16, 16, 16] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+  %2 = transform.structured.promote %1 { operands_to_promote = [0, 1], mapping = [#gpu.memory_space<private>] } : (!transform.any_op) -> !transform.any_op
 }
 
 
@@ -271,7 +271,7 @@ func.func @promote_rank_reducing_subviews(%arg0:  memref<?x?x?x64xf32, strided<[
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = transform.structured.promote %0
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = transform.structured.promote %0 : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/promotion_options.mlir b/mlir/test/Dialect/Linalg/promotion_options.mlir
index f18d448ae9c3..894831ac34c3 100644
--- a/mlir/test/Dialect/Linalg/promotion_options.mlir
+++ b/mlir/test/Dialect/Linalg/promotion_options.mlir
@@ -35,8 +35,8 @@ func.func @gemm(%a : memref<?x?xf32>, %b : memref<?x?xf32>, %c : memref<?x?xf32>
 //      CHECK:       memref.dealloc %[[tmpC]]
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1, %loops:3 = transform.structured.tile %0 [16, 16, 16] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-  %2 = transform.structured.promote %1 { operands_to_promote = [0, 2], force_full_tiles = [false, false], use_full_tiles_by_default }
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1, %loops:3 = transform.structured.tile %0 [16, 16, 16] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+  %2 = transform.structured.promote %1 { operands_to_promote = [0, 2], force_full_tiles = [false, false], use_full_tiles_by_default } : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/tile-to-foreach-thread.mlir b/mlir/test/Dialect/Linalg/tile-to-foreach-thread.mlir
index 731e8d0f5388..63065d5196ab 100644
--- a/mlir/test/Dialect/Linalg/tile-to-foreach-thread.mlir
+++ b/mlir/test/Dialect/Linalg/tile-to-foreach-thread.mlir
@@ -31,9 +31,10 @@ module {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb1(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
     %1:2 = transform.structured.tile_to_forall_op %0 num_threads [10, 20] (mapping = [ #gpu.thread<y>, #gpu.thread<x> ] )
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
   }
 }
 
@@ -73,10 +74,11 @@ func.func @matmul_tile_size_dynamic_dynamic(%A: tensor<?x?xf32>, %B: tensor<?x?x
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %sz = transform.structured.match ops{["test.dummy"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1:2 = transform.structured.tile_to_forall_op %0 tile_sizes %sz
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %sz = transform.structured.match ops{["test.dummy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1:2 = transform.structured.tile_to_forall_op %0 tile_sizes *(%sz : !transform.any_op)
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -112,9 +114,10 @@ func.func @matmul_static(%A: tensor<100x200xf32>, %B: tensor<200x300xf32>, %C: t
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:2 = transform.structured.tile_to_forall_op %0 num_threads [10, 21]
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 
@@ -153,9 +156,10 @@ func.func @matmul_tile_size_dynamic(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %C
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:2 = transform.structured.tile_to_forall_op %0 tile_sizes [10, 20]
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -189,9 +193,10 @@ func.func @matmul_tile_size_static(%A: tensor<100x200xf32>, %B: tensor<200x300xf
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:2 = transform.structured.tile_to_forall_op %0 tile_sizes [10, 21]
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -211,9 +216,10 @@ module {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb1(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
     %1:2 = transform.structured.tile_to_forall_op %0 num_threads [2] ( mapping = [#gpu.thread<x>])
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
   }
 }
 // CHECK-DAG: #[[$map0:.+]] = affine_map<(d0) -> (d0 * 2)>
@@ -261,10 +267,11 @@ func.func @matmul_tile_size_dynamic_dynamic(%A: tensor<?x?xf32>, %B: tensor<?x?x
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %sz = transform.structured.match ops{["test.dummy"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1:2 = transform.structured.tile_to_forall_op %0 tile_sizes [%sz, 20]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %sz = transform.structured.match ops{["test.dummy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1:2 = transform.structured.tile_to_forall_op %0 tile_sizes [%sz : !transform.any_op, 20]
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -315,9 +322,10 @@ transform.sequence failures(propagate) {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb1(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
     %forall, %tiled_generic = transform.structured.tile_to_forall_op %0 num_threads [7]
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
   }
 
 // -----
@@ -368,8 +376,9 @@ transform.sequence failures(propagate) {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%IN_MAT2: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.generic"]} in %IN_MAT2 : (!pdl.operation) -> !pdl.operation
+  ^bb1(%IN_MAT2: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.generic"]} in %IN_MAT2 : (!transform.any_op) -> !transform.any_op
     %forall, %tiled_generic = transform.structured.tile_to_forall_op %0 num_threads [4]
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
   }
 
diff --git a/mlir/test/Dialect/Linalg/transform-op-decompose.mlir b/mlir/test/Dialect/Linalg/transform-op-decompose.mlir
index 82795ec9d4bf..052992fa8711 100644
--- a/mlir/test/Dialect/Linalg/transform-op-decompose.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-decompose.mlir
@@ -200,7 +200,7 @@ func.func @pooling_nchw_max(%input: tensor<?x?x1x?xf32>, %filter: tensor<1x?xf32
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = transform.structured.decompose %0
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = transform.structured.decompose %0 : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir b/mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir
index 537ee8664df4..29cacb474b96 100644
--- a/mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir
@@ -42,12 +42,13 @@ module {
   func.func @dummy3() { return }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb1(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.op<"linalg.fill">
+    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!transform.any_op) -> !transform.op<"scf.forall">
 
     // linalg.fill is tileable. The op is tiled and fused.
     transform.structured.fuse_into_containing_op %0 into %1
+      : (!transform.op<"linalg.fill">, !transform.op<"scf.forall">) -> !transform.any_op
   }
 }
 
@@ -85,12 +86,13 @@ module {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.empty"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb1(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.empty"]} in %arg1 : (!transform.any_op) -> !transform.op<"tensor.empty">
+    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!transform.any_op) -> !transform.op<"scf.forall">
 
     // tensor.empty is not tileable. The op is cloned and fused.
     transform.structured.fuse_into_containing_op %0 into %1
+      : (!transform.op<"tensor.empty">, !transform.op<"scf.forall">) -> !transform.any_op
   }
 }
 
@@ -131,12 +133,13 @@ module {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb1(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.op<"linalg.fill">
+    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!transform.any_op) -> !transform.op<"scf.forall">
 
     // linalg.fill is tileable. The op is tiled and fused.
     transform.structured.fuse_into_containing_op %0 into %1
+      : (!transform.op<"linalg.fill">, !transform.op<"scf.forall">) -> !transform.any_op
   }
 }
 
@@ -179,12 +182,13 @@ module {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb1(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!transform.any_op) -> !transform.any_op
 
     // linalg.fill is tileable. The op is tiled and fused.
     transform.structured.fuse_into_containing_op %0 into %1
+      : (!transform.any_op, !transform.any_op) -> !transform.any_op
   }
 }
 
@@ -239,12 +243,13 @@ module {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb1(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.op<"linalg.generic">
+    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!transform.any_op) -> !transform.op<"scf.forall">
 
     // linalg.generic is tileable. The op is tiled and fused.
     transform.structured.fuse_into_containing_op %0 into %1
+      : (!transform.op<"linalg.generic">, !transform.op<"scf.forall">) -> !transform.any_op
   }
 }
 
@@ -273,13 +278,13 @@ module {
 
   transform.sequence failures(propagate) {
   ^bb1(%arg1: !transform.any_op):
-    %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !pdl.operation
-    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!transform.any_op) -> !pdl.operation
+    %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.structured.match ops{["scf.forall"]} in %arg1 : (!transform.any_op) -> !transform.any_op
 
     // Create a new handle that points to `linalg.fill` twice.
-    %2 = transform.merge_handles %0, %0 : !pdl.operation
+    %2 = transform.merge_handles %0, %0 : !transform.any_op
 
     // It shouldn't be a problem to fuse this handle.
-    transform.structured.fuse_into_containing_op %2 into %1
+    transform.structured.fuse_into_containing_op %2 into %1 : (!transform.any_op, !transform.any_op) -> !transform.any_op
   }
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-fuse.mlir b/mlir/test/Dialect/Linalg/transform-op-fuse.mlir
index 66328a18fa5d..20beefb351f8 100644
--- a/mlir/test/Dialect/Linalg/transform-op-fuse.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-fuse.mlir
@@ -16,9 +16,10 @@ func.func @fuse_unary(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.elemwise_binary"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.elemwise_binary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [32, 32], tile_interchange = [0, 1]}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -43,11 +44,11 @@ func.func @fuse_unary(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.elemwise_binary"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.elemwise_binary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [32, 32], tile_interchange = [0, 1]}
-  %loop = transform.cast %loops#0 : !pdl.operation to !transform.op<"scf.for">
-  transform.loop.peel %loop : (!transform.op<"scf.for">) -> !pdl.operation
+    : (!transform.any_op) -> (!transform.any_op, !transform.op<"scf.for">, !transform.any_op)
+  transform.loop.peel %loops#0 : (!transform.op<"scf.for">) -> !transform.any_op
 }
 
 // -----
@@ -86,10 +87,12 @@ func.func @interchange_reduction(%input: tensor<12x7x25xf32>) -> tensor<12x25xf3
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [5, 0, 7], tile_interchange = [0, 2, 1]}
-  %2, %loops_2 = transform.structured.tile %1 [0, 4] : (!pdl.operation) -> (!pdl.operation, !pdl.operation)
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
+  %2, %loops_2 = transform.structured.tile %1 [0, 4]
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -110,7 +113,8 @@ func.func @unpack_elemwise(%arg0: tensor<16x48x8x8xf32>, %arg1: tensor<128x384xf
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.elemwise_unary"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.elemwise_unary"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1, %loops:2 = transform.structured.fuse %0 {tile_sizes = [16, 32], tile_interchange = [0, 1]}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-generalize.mlir b/mlir/test/Dialect/Linalg/transform-op-generalize.mlir
index 17dd3a350893..470f98eb3a94 100644
--- a/mlir/test/Dialect/Linalg/transform-op-generalize.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-generalize.mlir
@@ -26,7 +26,7 @@ func.func @map_no_inputs(%input: tensor<16x32x64xf32>,
   func.return %reduce : tensor<16x64xf32>
 }
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = transform.structured.generalize %0
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = transform.structured.generalize %0 : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-hoist-pad-build-packing-loop-nest.mlir b/mlir/test/Dialect/Linalg/transform-op-hoist-pad-build-packing-loop-nest.mlir
index 99090ac9e157..996686616f85 100644
--- a/mlir/test/Dialect/Linalg/transform-op-hoist-pad-build-packing-loop-nest.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-hoist-pad-build-packing-loop-nest.mlir
@@ -11,25 +11,25 @@ func.func @pad_and_hoist_rhs(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
+^bb1(%arg1: !transform.any_op):
   %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
-  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5]
+  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   // In this case, the pad op is actually empty: we only tile the first dimension
   // and it does not have an impact on the RHS operand.
   %pad = transform.get_producer_of_operand %matmul_padded[1]
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   // expected-error @below {{requires exactly 2 non-null handles}}
   transform.structured.hoist_pad.build_packing_loop_nest %pad above %loops_l1
-     : (!pdl.operation, !pdl.operation) -> !pdl.operation
+     : (!transform.any_op, !transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -43,24 +43,24 @@ func.func @pad_and_hoist_init(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
+^bb1(%arg1: !transform.any_op):
   %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
-  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5]
+  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   %pad = transform.get_producer_of_operand %matmul_padded[2]
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   // We do not know yet how to hoist the init.
   // expected-error @below {{could not build packing loop nest}}
   transform.structured.hoist_pad.build_packing_loop_nest %pad above %loops_l1
-     : (!pdl.operation, !pdl.operation) -> !pdl.operation
+     : (!transform.any_op, !transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -73,7 +73,7 @@ func.func @pad_and_hoist_lhs(
   //     BUILD-PACKING-LOOP-NEST: %[[PACKED:.*]] = scf.for %{{.*}} -> (tensor<?x5x12xf32>) {
   //     BUILD-PACKING-LOOP-NEST:   tensor.pad %{{.*}} 
   //     BUILD-PACKING-LOOP-NEST:     : tensor<?x12xf32> to tensor<5x12xf32>
-  //     BUILD-PACKING-LOOP-NEST:   tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 5, 12] [1, 1, 1] 
+  //     BUILD-PACKING-LOOP-NEST:   tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 5, 12] [1, 1, 1]
   // BUILD-PACKING-LOOP-NEST-SAME:   : tensor<5x12xf32> into tensor<?x5x12xf32>
   //     BUILD-PACKING-LOOP-NEST: scf.for %{{.*}} -> (tensor<24x25xf32>)
   %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
@@ -81,22 +81,22 @@ func.func @pad_and_hoist_lhs(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
+^bb1(%arg1: !transform.any_op):
   %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
-  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5]
+  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   %pad = transform.get_producer_of_operand %matmul_padded[0]
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   transform.structured.hoist_pad.build_packing_loop_nest %pad above %loops_l1
-     : (!pdl.operation, !pdl.operation) -> !pdl.operation
+     : (!transform.any_op, !transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -107,11 +107,11 @@ func.func @pad_and_hoist_lhs_transpose(
      -> tensor<24x25xf32>
 {
   //     BUILD-PACKING-LOOP-NEST: %[[PACKED:.*]] = scf.for %{{.*}} -> (tensor<?x12x5xf32>) {
-  //     BUILD-PACKING-LOOP-NEST:   tensor.pad %{{.*}} 
+  //     BUILD-PACKING-LOOP-NEST:   tensor.pad %{{.*}}
   //     BUILD-PACKING-LOOP-NEST:     : tensor<?x12xf32> to tensor<5x12xf32>
   //     BUILD-PACKING-LOOP-NEST:   linalg.generic
   //     BUILD-PACKING-LOOP-NEST:     -> tensor<12x5xf32>
-  //     BUILD-PACKING-LOOP-NEST:   tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 12, 5] [1, 1, 1] 
+  //     BUILD-PACKING-LOOP-NEST:   tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 12, 5] [1, 1, 1]
   // BUILD-PACKING-LOOP-NEST-SAME:   : tensor<12x5xf32> into tensor<?x12x5xf32>
   //     BUILD-PACKING-LOOP-NEST: scf.for %{{.*}} -> (tensor<24x25xf32>)
   %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
@@ -119,22 +119,22 @@ func.func @pad_and_hoist_lhs_transpose(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
+^bb1(%arg1: !transform.any_op):
   %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
-  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5]
+  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   %pad = transform.get_producer_of_operand %matmul_padded[0]
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   transform.structured.hoist_pad.build_packing_loop_nest %pad above %loops_l1, transpose by [1, 0]
-     : (!pdl.operation, !pdl.operation) -> !pdl.operation
+     : (!transform.any_op, !transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -147,7 +147,7 @@ func.func @pad_and_hoist_init(
 
   //      BUILD-PACKING-LOOP-NEST: scf.for %{{.*}} -> (tensor<24x25xf32>) {
   //      BUILD-PACKING-LOOP-NEST:   %[[EXTRACTED_SLICE:.*]] = tensor.extract_slice
-  //      BUILD-PACKING-LOOP-NEST:   %[[PADDED:.*]] = tensor.pad %[[EXTRACTED_SLICE]] 
+  //      BUILD-PACKING-LOOP-NEST:   %[[PADDED:.*]] = tensor.pad %[[EXTRACTED_SLICE]]
   //      BUILD-PACKING-LOOP-NEST:     : tensor<?x25xf32> to tensor<5x25xf32>
   //      BUILD-PACKING-LOOP-NEST:   scf.for %{{.*}} iter_args({{.*}} = %[[EXTRACTED_SLICE]]) -> (tensor<24x25xf32>, tensor<?x25xf32>) {
   %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
@@ -155,20 +155,20 @@ func.func @pad_and_hoist_init(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
+^bb1(%arg1: !transform.any_op):
   %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
-  %matmul_l1, %loops_l1:2 = transform.structured.tile_to_scf_for %matmul [5, 0, 7]
+  %matmul_l1, %loops_l1:2 = transform.structured.tile_to_scf_for %matmul [5, 0, 7] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   %pad = transform.get_producer_of_operand %matmul_padded[2]
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   transform.structured.hoist_pad.build_packing_loop_nest %pad above %loops_l1#1
-     : (!pdl.operation, !pdl.operation) -> !pdl.operation
+     : (!transform.any_op, !transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-hoist-pad.mlir b/mlir/test/Dialect/Linalg/transform-op-hoist-pad.mlir
index 871163ab40cf..33801e2a80d9 100644
--- a/mlir/test/Dialect/Linalg/transform-op-hoist-pad.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-hoist-pad.mlir
@@ -2,7 +2,7 @@
 
 func.func @pad_and_hoist_rhs(
   %arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-     -> tensor<24x25xf32> 
+     -> tensor<24x25xf32>
 {
   // expected-note @below {{payload operation}}
   %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
@@ -10,34 +10,34 @@ func.func @pad_and_hoist_rhs(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 
-    : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
+    : (!transform.any_op) -> !transform.any_op
 
-  
-  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5]
+
+  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   // In this case, the pad op is actually empty: we only tile the first dimension
   // and it does not have an impact on the RHS operand.
   // expected-error @below {{incompatible payload operation name}}
   %pad = transform.get_producer_of_operand %matmul_padded[1]
-    : (!pdl.operation) -> !transform.op<"tensor.pad">
+    : (!transform.any_op) -> !transform.op<"tensor.pad">
 
   // We do not even reach this transform op.
   transform.structured.hoist_pad %pad by 1 loops
-     : (!transform.op<"tensor.pad">) -> !pdl.operation
+     : (!transform.op<"tensor.pad">) -> !transform.any_op
 }
 
 // -----
 
 func.func @pad_and_hoist_init(
   %arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-     -> tensor<24x25xf32> 
+     -> tensor<24x25xf32>
 {
   // expected-note @below {{when applied to this op}}
   %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
@@ -45,25 +45,25 @@ func.func @pad_and_hoist_init(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 
-    : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
+    : (!transform.any_op) -> !transform.any_op
+
 
-  
-  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5]
+  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   %pad = transform.get_producer_of_operand %matmul_padded[2]
-    : (!pdl.operation) -> !transform.op<"tensor.pad">
+    : (!transform.any_op) -> !transform.op<"tensor.pad">
 
   // We do not know yet how to hoist the init.
   // expected-error @below {{transform.structured.hoist_pad failed to apply}}
   transform.structured.hoist_pad %pad by 1 loops
-     : (!transform.op<"tensor.pad">) -> !pdl.operation
+     : (!transform.op<"tensor.pad">) -> !transform.any_op
 }
 
 // -----
@@ -71,15 +71,15 @@ transform.sequence failures(propagate) {
 //     CHECK-LABEL: pad_and_hoist_lhs(
 func.func @pad_and_hoist_lhs(
   %arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-     -> tensor<24x25xf32> 
+     -> tensor<24x25xf32>
 {
   //     CHECK: %[[PACKED:.*]] = scf.for %{{.*}} -> (tensor<5x5x12xf32>) {
-  //     CHECK:   tensor.pad %{{.*}} 
+  //     CHECK:   tensor.pad %{{.*}}
   //     CHECK:     : tensor<?x12xf32> to tensor<5x12xf32>
-  //     CHECK:   tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 5, 12] [1, 1, 1] 
+  //     CHECK:   tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 5, 12] [1, 1, 1]
   // CHECK-SAME:   : tensor<5x12xf32> into tensor<5x5x12xf32>
   //     CHECK: scf.for %{{.*}} -> (tensor<24x25xf32>) {
-  //     CHECK:   %[[PADDED:.*]] = tensor.extract_slice %[[PACKED]][%{{.*}}, 0, 0] [1, 5, 12] [1, 1, 1] 
+  //     CHECK:   %[[PADDED:.*]] = tensor.extract_slice %[[PACKED]][%{{.*}}, 0, 0] [1, 5, 12] [1, 1, 1]
   // CHECK-SAME:    : tensor<5x5x12xf32> to tensor<5x12xf32>
   //     CHECK:   linalg.matmul ins(%[[PADDED]]
   %0 = linalg.matmul ins(%arg0, %arg1 : tensor<24x12xf32>, tensor<12x25xf32>) outs(%arg2 : tensor<24x25xf32>) -> tensor<24x25xf32>
@@ -87,23 +87,23 @@ func.func @pad_and_hoist_lhs(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 
-    : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
+    : (!transform.any_op) -> !transform.any_op
+
 
-  
-  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5]
+  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   %pad = transform.get_producer_of_operand %matmul_padded[0]
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   transform.structured.hoist_pad %pad by 1 loops
-     : (!pdl.operation) -> !pdl.operation
+     : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -111,17 +111,17 @@ transform.sequence failures(propagate) {
 //     CHECK-LABEL: pad_and_hoist_lhs_transpose
 func.func @pad_and_hoist_lhs_transpose(
   %arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-     -> tensor<24x25xf32> 
+     -> tensor<24x25xf32>
 {
   //     CHECK: %[[PACKED:.*]] = scf.for %{{.*}} -> (tensor<5x12x5xf32>) {
-  //     CHECK:   tensor.pad %{{.*}} 
+  //     CHECK:   tensor.pad %{{.*}}
   //     CHECK:     : tensor<?x12xf32> to tensor<5x12xf32>
   //     CHECK:   linalg.generic
   //     CHECK:     -> tensor<12x5xf32>
-  //     CHECK:   tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 12, 5] [1, 1, 1] 
+  //     CHECK:   tensor.insert_slice %{{.*}} into %{{.*}}[%{{.*}}, 0, 0] [1, 12, 5] [1, 1, 1]
   // CHECK-SAME:   : tensor<12x5xf32> into tensor<5x12x5xf32>
   //     CHECK: scf.for %{{.*}} -> (tensor<24x25xf32>) {
-  //     CHECK:   %[[PADDED:.*]] = tensor.extract_slice %[[PACKED]][%{{.*}}, 0, 0] [1, 12, 5] [1, 1, 1] 
+  //     CHECK:   %[[PADDED:.*]] = tensor.extract_slice %[[PACKED]][%{{.*}}, 0, 0] [1, 12, 5] [1, 1, 1]
   // CHECK-SAME:    : tensor<5x12x5xf32> to tensor<12x5xf32>
   //     CHECK:   %[[TRANSPOSED:.*]] = linalg.generic
   //     CHECK:     -> tensor<5x12xf32>
@@ -131,23 +131,23 @@ func.func @pad_and_hoist_lhs_transpose(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 
-    : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
+    : (!transform.any_op) -> !transform.any_op
 
-  
-  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5]
+
+  %matmul_l1, %loops_l1 = transform.structured.tile_to_scf_for %matmul [5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   %pad = transform.get_producer_of_operand %matmul_padded[0]
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   transform.structured.hoist_pad %pad by 1 loops, transpose by [1, 0]
-     : (!pdl.operation) -> !pdl.operation
+     : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -155,11 +155,11 @@ transform.sequence failures(propagate) {
 //     CHECK-LABEL: pad_and_hoist_init
 func.func @pad_and_hoist_init(
   %arg0: tensor<24x12xf32>, %arg1: tensor<12x25xf32>, %arg2: tensor<24x25xf32>)
-     -> tensor<24x25xf32> 
+     -> tensor<24x25xf32>
 {
 
   //      CHECK: scf.for %{{.*}} -> (tensor<24x25xf32>) {
-  //      CHECK:   %[[PADDED:.*]] = tensor.pad %{{.*}} 
+  //      CHECK:   %[[PADDED:.*]] = tensor.pad %{{.*}}
   //      CHECK:     : tensor<?x25xf32> to tensor<5x25xf32>
   //      CHECK:   %[[SCF_YIELD:.*]] = scf.for %{{.*}} iter_args(%[[INNER_PADDED:[0-9a-zA-Z]*]] = %[[PADDED]]) -> (tensor<5x25xf32>)
   //      CHECK:     %[[RES:.*]] = linalg.matmul {{.*}} outs(%[[INNER_PADDED]]
@@ -174,21 +174,21 @@ func.func @pad_and_hoist_init(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 
-    : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1
+    : (!transform.any_op) -> !transform.any_op
+
 
-  
-  %matmul_l1, %loops_l1:2 = transform.structured.tile_to_scf_for %matmul [5, 0, 7]
+  %matmul_l1, %loops_l1:2 = transform.structured.tile_to_scf_for %matmul [5, 0, 7] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 
   %matmul_padded = transform.structured.pad %matmul_l1 {
     padding_values=[0.0: f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 
   %pad = transform.get_producer_of_operand %matmul_padded[2]
-    : (!pdl.operation) -> !transform.op<"tensor.pad">
+    : (!transform.any_op) -> !transform.op<"tensor.pad">
 
   transform.structured.hoist_pad %pad by 1 loops
-     : (!transform.op<"tensor.pad">) -> !pdl.operation
+     : (!transform.op<"tensor.pad">) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-interchange.mlir b/mlir/test/Dialect/Linalg/transform-op-interchange.mlir
index 52b636c6bd3f..7966b22a257a 100644
--- a/mlir/test/Dialect/Linalg/transform-op-interchange.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-interchange.mlir
@@ -19,9 +19,9 @@ func.func @interchange_generic(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.interchange %0 iterator_interchange = [1, 0] 
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.interchange %0 iterator_interchange = [1, 0] : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -33,8 +33,8 @@ func.func @interchange_matmul(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>, %a
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   // expected-error @below {{transform applied to the wrong op kind}}
-  transform.structured.interchange %0 iterator_interchange = [1, 0]
+  transform.structured.interchange %0 iterator_interchange = [1, 0] : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir b/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir
index 910f019f1a58..e60b89a5c406 100644
--- a/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir
@@ -28,9 +28,9 @@ func.func @outerproduct_matmul(%A: memref<3x3xf32>, %B: memref<3x3xf32>, %C: mem
 // CHECK:         }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
-  transform.vector.lower_contraction %2 lowering_strategy = "outerproduct" : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+  transform.vector.lower_contraction %2 lowering_strategy = "outerproduct" : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-pack.mlir b/mlir/test/Dialect/Linalg/transform-op-pack.mlir
index b2796e335432..eaeb258590d7 100644
--- a/mlir/test/Dialect/Linalg/transform-op-pack.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-pack.mlir
@@ -34,10 +34,10 @@ func.func @reduction_2d_static(%t0: tensor<3x7xf16>, %t1: tensor<3xf16>) -> tens
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   transform.structured.pack %0 packed_sizes = [0, 4]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 }
 
 // -----
@@ -76,17 +76,17 @@ func.func @col_reduction_2d_static(%t0: tensor<7x3xf16>, %t1: tensor<3xf16>) ->
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1 = transform.structured.pack %0 packed_sizes = [4, 0]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
   %pack = transform.get_producer_of_operand %1[0] 
     : (!transform.op<"linalg.generic">) -> (!transform.op<"tensor.pack">)
   %2, %pack_2, %empty_unpack_2 = 
     transform.structured.pack_transpose %pack with_compute_op(%1) 
     outer_perm = [1, 0]
      : (!transform.op<"tensor.pack">, !transform.op<"linalg.generic">) 
-    -> (!transform.op<"linalg.generic">, !transform.op<"tensor.pack">, !pdl.operation)
+    -> (!transform.op<"linalg.generic">, !transform.op<"tensor.pack">, !transform.any_op)
 }
 
 // -----
@@ -131,10 +131,10 @@ func.func @reduction_2d_dynamic(%t0: tensor<?x?xf16>, %t1: tensor<?xf16>) -> ten
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   transform.structured.pack %0 packed_sizes = [0, 4]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 }
 
 
@@ -177,10 +177,10 @@ func.func @reduction_2d_dynamic(%t0: tensor<?x?xf16>, %t1: tensor<?xf16>) -> ten
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   transform.structured.pack %0 packed_sizes = [3, 4]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 }
 
 // -----
@@ -220,11 +220,11 @@ func.func @matmul(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tensor<?x?xf32>)
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
     //                                            M  N  K
     %1 = transform.structured.pack %0 packed_sizes = [2, 3, 4]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 
     %unpack = transform.get_consumers_of_result %1[0] 
       : (!transform.op<"linalg.generic">) -> (!transform.op<"tensor.unpack">)
@@ -268,11 +268,11 @@ func.func @conv_2d_nchw_fchw(%i: tensor<14x512x28x28xf32>, %f: tensor<1024x512x1
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
   //                                            N  F  H  W  C KH KW
   %1 = transform.structured.pack %0 packed_sizes = [0, 4, 0, 0, 8, 0, 0]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 }
 
 // -----
@@ -309,11 +309,11 @@ func.func @conv_2d_nhwc_hwcf(%input: tensor<?x1x?x?xf32>, %filter: tensor<1x?x?x
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
   //                                            N  H  W  F KH KW  C
   %1 = transform.structured.pack %0 packed_sizes = [0, 0, 0, 4, 0, 0, 6]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 }
 
 // -----
@@ -355,11 +355,11 @@ func.func @matmul_dynamic_pack_size(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %C
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %sz = transform.structured.match ops{["some_tile_size"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.pack %0 packed_sizes = [0, %sz, %sz] 
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %sz = transform.structured.match ops{["some_tile_size"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.structured.pack %0 packed_sizes = [0, %sz : !transform.any_op, %sz : !transform.any_op] 
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 }
 
 // -----
@@ -372,12 +372,12 @@ func.func @conv_cant_pack(%i: tensor<14x512x28x28xf32>, %f: tensor<1024x512x1x1x
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
   //                                                N  F  H  W  C KH KW
   // expected-error @below {{data tiling failed}}
   %1 = transform.structured.pack %0 packed_sizes = [0, 0, 4, 0, 0, 0, 0]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 }
 
 // -----
@@ -394,11 +394,11 @@ func.func @matmul(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tensor<?x?xf32>)
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
     // expected-error @below {{requires target to map to exactly 1 LinalgOp (got 2)}}
     %1 = transform.structured.pack %0 packed_sizes = [2, 3, 4] 
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 }
 
 
@@ -413,11 +413,11 @@ func.func @matmul(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tensor<?x?xf32>)
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
     // expected-error @below {{requires number of packed sizes match the number of loops (2 vs 3)}}
     %1 = transform.structured.pack %0 packed_sizes = [2, 3] 
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 }
 
 // -----
@@ -430,14 +430,14 @@ func.func @no_single_packing_op(%source: tensor<128x256xf32>, %dest: tensor<4x16
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
       // expected-error @below {{requires target to map to exactly 1 packing op and 1 packed op (got 2 and 1)}}
     transform.structured.pack_transpose %0 with_compute_op(%1) 
     inner_perm = [0]
-      : (!pdl.operation, !pdl.operation) 
-      -> (!pdl.operation, !pdl.operation, !pdl.operation)
+      : (!transform.any_op, !transform.any_op) 
+      -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -449,14 +449,14 @@ func.func @no_single_pack_unpack(%source: tensor<128x256xf32>, %dest: tensor<4x1
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["arith.constant"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["tensor.empty"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["arith.constant"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.structured.match ops{["tensor.empty"]} in %arg1 : (!transform.any_op) -> !transform.any_op
       // expected-error @below {{requires target to map to a tensor.pack or tensor.unpack}}
     transform.structured.pack_transpose %0 with_compute_op(%1) 
     inner_perm = [0]
-      : (!pdl.operation, !pdl.operation) 
-      -> (!pdl.operation, !pdl.operation, !pdl.operation)
+      : (!transform.any_op, !transform.any_op) 
+      -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -468,14 +468,14 @@ func.func @no_linalg_target(%source: tensor<128x256xf32>, %dest: tensor<4x16x32x
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["arith.constant"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.structured.match ops{["arith.constant"]} in %arg1 : (!transform.any_op) -> !transform.any_op
       // expected-error @below {{requires a LinalgOp target}}
     transform.structured.pack_transpose %0 with_compute_op(%1) 
     inner_perm = [0]
-      : (!pdl.operation, !pdl.operation) 
-      -> (!pdl.operation, !pdl.operation, !pdl.operation)
+      : (!transform.any_op, !transform.any_op) 
+      -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -489,14 +489,14 @@ func.func @no_single_use_by_linalg(%source: tensor<128x256xf32>, %dest: tensor<4
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
       // expected-error @below {{not a single use by the LinalgOp target}}
     transform.structured.pack_transpose %0 with_compute_op(%1) 
     inner_perm = [0]
-      : (!pdl.operation, !pdl.operation) 
-      -> (!pdl.operation, !pdl.operation, !pdl.operation)
+      : (!transform.any_op, !transform.any_op) 
+      -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -511,14 +511,14 @@ func.func @not_produced_by_linalg(%source: tensor<128x256xf32>, %dest: tensor<4x
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
       // expected-error @below {{not produced by the LinalgOp target}}
     transform.structured.pack_transpose %0 with_compute_op(%1) 
     inner_perm = [0]
-      : (!pdl.operation, !pdl.operation) 
-      -> (!pdl.operation, !pdl.operation, !pdl.operation)
+      : (!transform.any_op, !transform.any_op) 
+      -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -532,14 +532,14 @@ func.func @no_matching_pack(%source: tensor<16xf32>) {
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
       // expected-error @below {{could not find matching pack op}}
     transform.structured.pack_transpose %0 with_compute_op(%1) 
     inner_perm = [0]
-      : (!pdl.operation, !pdl.operation) 
-      -> (!pdl.operation, !pdl.operation, !pdl.operation)
+      : (!transform.any_op, !transform.any_op) 
+      -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -553,10 +553,10 @@ func.func @invalid_outer_perm(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tens
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
     %1 = transform.structured.pack %0 packed_sizes = [2, 3, 4]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 
     %unpack = transform.get_consumers_of_result %1[0] 
       : (!transform.op<"linalg.generic">) -> (!transform.op<"tensor.unpack">)
@@ -579,10 +579,10 @@ func.func @invalid_inner_perm(%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %C: tens
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
     %1 = transform.structured.pack %0 packed_sizes = [2, 3, 4]
-      : (!pdl.operation) -> (!transform.op<"linalg.generic">)
+      : (!transform.any_op) -> (!transform.op<"linalg.generic">)
 
     %unpack = transform.get_consumers_of_result %1[0] 
       : (!transform.op<"linalg.generic">) -> (!transform.op<"tensor.unpack">)
diff --git a/mlir/test/Dialect/Linalg/transform-op-pad.mlir b/mlir/test/Dialect/Linalg/transform-op-pad.mlir
index 3677c1afeecb..78197aa7098d 100644
--- a/mlir/test/Dialect/Linalg/transform-op-pad.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-pad.mlir
@@ -32,13 +32,13 @@ func.func @static_sizes_output_divisible(%arg0: tensor<24x12xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1 = transform.structured.pad %0 {
-    padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32], 
-    padding_dimensions=[0, 1, 2], 
+    padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
+    padding_dimensions=[0, 1, 2],
     pack_paddings=[1, 1, 0]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -74,13 +74,13 @@ func.func @static_sizes_output_divisible_on_empty_op(%arg0: tensor<24x12xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1 = transform.structured.pad %0 {
     padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2],
     pack_paddings=[1, 1, 0]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -94,14 +94,14 @@ func.func @pad(%arg0: tensor<24x12xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   // expected-error @below {{op expects a padding value of type 'f32', got 0 : i32}}
   %1 = transform.structured.pad %0 {
     padding_values=[0: i32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2],
     pack_paddings=[1, 1, 0]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -115,14 +115,14 @@ func.func @pad(%arg0: tensor<24x12xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   // expected-error @below {{expects a padding that parses to 'f32', got "{foo}"}}
   %1 = transform.structured.pad %0 {
     padding_values=["{foo}", 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2],
     pack_paddings=[1, 1, 0]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -138,15 +138,15 @@ func.func @pad(%arg0: tensor<24x12xf32>,
 }
 
 transform.sequence failures(suppress) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   // This error is silenceable and is not reported by this transform
   //   {{transform.structured.pad failed to apply}}
   %1 = transform.structured.pad %0 {
     padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2],
     pack_paddings=[1, 1, 0]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -200,11 +200,11 @@ func.func @outs_not_produced_by_empty_or_extract_slice(%a : tensor<128x2044xf32>
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1 = transform.structured.pad %0 {
     padding_values=[0.0 : f32, 0.0 : f32, 0.0 : f32],
     padding_dimensions=[0, 1, 2],
     pack_paddings=[1, 1, 1]
-  }
+  } : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-replace.mlir b/mlir/test/Dialect/Linalg/transform-op-replace.mlir
index 3d30a2cfe14f..3c6a4eb60530 100644
--- a/mlir/test/Dialect/Linalg/transform-op-replace.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-replace.mlir
@@ -9,13 +9,13 @@ func.func @bar() {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["func.func"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["func.func"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   transform.structured.replace %0 {
     func.func @foo() {
       "dummy_op"() : () -> ()
     }
-  }
+  } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -25,12 +25,12 @@ func.func @bar(%arg0: i1) {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["another_op"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["another_op"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   // expected-error @+1 {{expected target without operands}}
   transform.structured.replace %0 {
     "dummy_op"() : () -> ()
-  }
+  } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -40,11 +40,11 @@ func.func @bar() {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["another_op"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["another_op"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   transform.structured.replace %0 {
   ^bb0(%a: i1):
     // expected-error @+1 {{expected replacement without operands}}
     "dummy_op"(%a) : (i1) -> ()
-  }
+  } : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-scalarize.mlir b/mlir/test/Dialect/Linalg/transform-op-scalarize.mlir
index 272d0b50333a..40a1b149837d 100644
--- a/mlir/test/Dialect/Linalg/transform-op-scalarize.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-scalarize.mlir
@@ -19,8 +19,8 @@ func.func @scalarize(%arg0: tensor<24x12xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1, %loops = transform.structured.tile %0 [10, 0, 0] : (!pdl.operation) -> (!pdl.operation, !pdl.operation)
-  %2 = transform.structured.scalarize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1, %loops = transform.structured.tile %0 [10, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+  %2 = transform.structured.scalarize %1 : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-split-reduction-by-scaling.mlir b/mlir/test/Dialect/Linalg/transform-op-split-reduction-by-scaling.mlir
index 974a2b763b06..719e0daccfbf 100644
--- a/mlir/test/Dialect/Linalg/transform-op-split-reduction-by-scaling.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-split-reduction-by-scaling.mlir
@@ -19,8 +19,9 @@ func.func @matmul_split(%A : tensor<?x256xf32>, %B: tensor<256x32xf32>, %C: tens
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:4 = transform.structured.split_reduction %0
     { split_factor = 4, insert_split_dimension = 2, use_scaling_algorithm, use_alloc}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir b/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
index e5615d54ca7d..9d8f2ed5640b 100644
--- a/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
@@ -32,9 +32,10 @@ func.func @matmul_split(%A : tensor<16x256xf32>, %B: tensor<256x32xf32>, %C: ten
 //      CHECK: return %[[R]] : tensor<16x32xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -80,9 +81,10 @@ func.func @generic_split_1d(%arg0: tensor<32xf32>, %arg1: tensor<f32>, %out: ten
 //      CHECK: return %[[R]] : tensor<f32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 0}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -131,9 +133,10 @@ func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>
 //      CHECK: return %[[R]] : tensor<5x2xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -170,9 +173,10 @@ func.func @matmul_split(%A : tensor<16x256xf32>, %B: tensor<256x32xf32>, %C: ten
 //      CHECK: return %[[R]] : tensor<16x32xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2, inner_parallel}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -218,9 +222,10 @@ func.func @generic_split_1d(%arg0: tensor<32xf32>, %arg1: tensor<f32>, %out: ten
 //      CHECK: return %[[R]] : tensor<f32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 0, inner_parallel}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -269,7 +274,8 @@ func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>
 //      CHECK: return %[[R]] : tensor<5x2xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2, inner_parallel}
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-tile.mlir b/mlir/test/Dialect/Linalg/transform-op-tile.mlir
index 15e8f8c41eba..1ad9383069b1 100644
--- a/mlir/test/Dialect/Linalg/transform-op-tile.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-tile.mlir
@@ -128,14 +128,14 @@ func.func @tile_linalg_matmul(
 
 // CHECK-LABEL: tile_tensor_pad
 func.func @tile_tensor_pad(
-  %arg0 : tensor<?x?xf32>, %cst : f32, %low: index, %high: index) 
+  %arg0 : tensor<?x?xf32>, %cst : f32, %low: index, %high: index)
     -> tensor<20x40xf32>
 {
   // CHECK: scf.forall
   // CHECK:   scf.if
   // CHECK:     tensor.generate
   // CHECK:   else
-  // CHECK:     tensor.pad {{.*}} nofold 
+  // CHECK:     tensor.pad {{.*}} nofold
   %0 = tensor.pad %arg0 nofold low[%low, %low] high[%high, %high] {
         ^bb0(%arg9: index, %arg10: index):
           tensor.yield %cst : f32
@@ -144,7 +144,8 @@ func.func @tile_tensor_pad(
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   transform.structured.tile_to_forall_op %0 tile_sizes[1, 1]
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir b/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir
index 155b0785d2ec..ecc077ae5e9f 100644
--- a/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir
@@ -17,10 +17,10 @@ func.func @vectorize_matmul(%arg0: tensor<24x12xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -63,10 +63,10 @@ func.func @vectorize_keep_pad(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -111,10 +111,10 @@ func.func @vectorize_pad(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1 {vectorize_padding}
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 {vectorize_padding} : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -128,8 +128,8 @@ func.func @vectorize(%arg0: tensor<24x12xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   // expected-error @below {{op requires isolated-from-above targets}}
-  %2 = transform.structured.vectorize %0
+  %2 = transform.structured.vectorize %0 : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-ops-invalid.mlir b/mlir/test/Dialect/Linalg/transform-ops-invalid.mlir
index 2adfd9ae97aa..b671f20c1047 100644
--- a/mlir/test/Dialect/Linalg/transform-ops-invalid.mlir
+++ b/mlir/test/Dialect/Linalg/transform-ops-invalid.mlir
@@ -1,73 +1,89 @@
 // RUN: mlir-opt %s --split-input-file --verify-diagnostics
 
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   // expected-error@below {{'transform.structured.interchange' op expects iterator_interchange to be a permutation, found 1, 1}}
-  transform.structured.interchange %arg0 iterator_interchange = [1, 1] 
+  transform.structured.interchange %arg0 iterator_interchange = [1, 1] : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
 
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   // expected-error@below {{expects padding_dimensions to contain positive integers, found [1, -7]}}
-  transform.structured.pad %arg0 {padding_dimensions=[1, -7]}
+  transform.structured.pad %arg0 {padding_dimensions=[1, -7]} : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
 
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   // expected-error@below {{expects pack_paddings to contain booleans (0/1), found [1, 7]}}
-  transform.structured.pad %arg0 {pack_paddings=[1, 7]}
+  transform.structured.pad %arg0 {pack_paddings=[1, 7]} : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
 
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   // expected-error@below {{expects transpose_paddings to be a permutation, found [1, 1]}}
-  transform.structured.pad %arg0 {transpose_paddings=[[1, 1]]}
+  transform.structured.pad %arg0 {transpose_paddings=[[1, 1]]} : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
 
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   // expected-error@below {{'transform.structured.interchange' op attribute 'iterator_interchange' failed to satisfy constraint: i64 dense array attribute whose value is non-negative}}
-  transform.structured.interchange %arg0 iterator_interchange = [-3, 1]
+  transform.structured.interchange %arg0 iterator_interchange = [-3, 1] : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
 
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   // expected-error@below {{expects all results type to be the same}}
   "transform.structured.multitile_sizes"(%arg0) { target_size = 3, divisor = 2, dimension = 0 }
-      : (!pdl.operation) -> (!transform.param<i64>, !transform.param<i64>, !transform.param<i32>)
+      : (!transform.any_op) -> (!transform.param<i64>, !transform.param<i64>, !transform.param<i32>)
 }
 
 // -----
 
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   // expected-error@below {{not a valid permutation}}
   transform.structured.pack_greedily %arg0
-      matmul_packed_sizes = [8, 0, 32] 
+      matmul_packed_sizes = [8, 0, 32]
       matmul_inner_dims_order = [1, 1, 0]
-    : (!pdl.operation) -> !transform.op<"linalg.generic">
+    : (!transform.any_op) -> !transform.op<"linalg.generic">
 
 }
 
 // -----
 
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   // expected-error@below {{at most one of the packed_size and the padded_sizes_next_multiple_of can be nonzero}}
   transform.structured.pack_greedily %arg0
-      matmul_packed_sizes = [1, 1, 1] 
-      matmul_padded_sizes_next_multiple_of = [1, 1, 1] 
+      matmul_packed_sizes = [1, 1, 1]
+      matmul_padded_sizes_next_multiple_of = [1, 1, 1]
       matmul_inner_dims_order = [0, 1, 2]
-    : (!pdl.operation) -> !transform.op<"linalg.generic">
+    : (!transform.any_op) -> !transform.op<"linalg.generic">
 
 }
+
+// -----
+
+transform.sequence failures(propagate) {
+^bb0(%arg0: !transform.any_op):
+  // expected-error @below {{expected 4 result types, got 2}}
+  transform.structured.tile_to_scf_for %arg0 [1, 2, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+}
+
+// -----
+
+transform.sequence failures(propagate) {
+^bb0(%arg0: !transform.any_op, %arg1: !transform.any_op):
+  // expected-error @below {{expected 2 operand types, got 1}}
+  transform.structured.tile_to_scf_for %arg0 [%arg1] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
+}
diff --git a/mlir/test/Dialect/Linalg/transform-ops.mlir b/mlir/test/Dialect/Linalg/transform-ops.mlir
index 4fa426e1394e..dd850087ba7f 100644
--- a/mlir/test/Dialect/Linalg/transform-ops.mlir
+++ b/mlir/test/Dialect/Linalg/transform-ops.mlir
@@ -1,9 +1,9 @@
 // RUN: mlir-opt %s | mlir-opt | FileCheck %s
 
 transform.sequence failures(propagate) {
-^bb1(%arg0: !pdl.operation):
+^bb1(%arg0: !transform.any_op):
   // CHECK %{{.*}}, %{{.*}}:2 = transform.structured.tile
-  %0, %1:2 = transform.structured.tile %arg0 [2, 0, 3] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation)
+  %0, %1:2 = transform.structured.tile %arg0 [2, 0, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 transform.sequence failures(propagate) {
@@ -19,19 +19,19 @@ transform.sequence failures(propagate) {
 //===----------------------------------------------------------------------===//
 
 transform.sequence failures(propagate) {
-^bb1(%arg0: !pdl.operation):
+^bb1(%arg0: !transform.any_op):
   // CHECK: transform.structured.pad
-  %0 = transform.structured.pad %arg0
+  %0 = transform.structured.pad %arg0 : (!transform.any_op) -> !transform.any_op
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg0: !pdl.operation):
+^bb1(%arg0: !transform.any_op):
   // CHECK: transform.structured.interchange
-  %0 = transform.structured.interchange %arg0
+  %0 = transform.structured.interchange %arg0 : (!transform.any_op) -> !transform.any_op
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg0: !pdl.operation):
+^bb1(%arg0: !transform.any_op):
   // CHECK: transform.structured.scalarize
-  %0 = transform.structured.scalarize %arg0
+  %0 = transform.structured.scalarize %arg0 : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Linalg/transform-patterns.mlir b/mlir/test/Dialect/Linalg/transform-patterns.mlir
index 6214e4e527f1..49a20b563aa6 100644
--- a/mlir/test/Dialect/Linalg/transform-patterns.mlir
+++ b/mlir/test/Dialect/Linalg/transform-patterns.mlir
@@ -10,9 +10,9 @@ func.func @dot(%x: memref<?xf32, strided<[1], offset: ?>>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.dot"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loop = transform.structured.tile %0 [8000] : (!pdl.operation) -> (!pdl.operation, !pdl.operation)
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.dot"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loop = transform.structured.tile %0 [8000] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 // CHECK-LABEL: func @dot
@@ -34,9 +34,9 @@ func.func @matvec(%A: memref<?x?xf32, strided<[?, 1], offset: ?>>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile %0 [5, 6] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation)
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile %0 [5, 6] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // CHECK-LABEL: func @matvec
@@ -61,12 +61,12 @@ func.func @matmul(%A: memref<?x?xf32, strided<[?, 1], offset: ?>>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:3 = transform.structured.tile %0 [2000, 3000, 4000] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-    %2, %loops_2:3 = transform.structured.tile %1 [200, 300, 400] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-    %3, %loops_3:3 = transform.structured.tile %2 [20, 30, 40] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-    %4, %loops_4:3 = transform.structured.tile %3 [2, 3, 4] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:3 = transform.structured.tile %0 [2000, 3000, 4000] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+    %2, %loops_2:3 = transform.structured.tile %1 [200, 300, 400] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+    %3, %loops_3:3 = transform.structured.tile %2 [20, 30, 40] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+    %4, %loops_4:3 = transform.structured.tile %3 [2, 3, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // CHECK-LABEL: func @matmul
@@ -134,9 +134,9 @@ func.func @permute_generic(%A: memref<?x?xf32, strided<[?, 1], offset: ?>>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.interchange %0 iterator_interchange = [1, 2, 0]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.interchange %0 iterator_interchange = [1, 2, 0] : (!transform.any_op) -> !transform.any_op
 }
 
 // CHECK-LABEL:  func @permute_generic
@@ -160,9 +160,9 @@ func.func @matvec_perm(%A: memref<?x?xf32, strided<[?, 1], offset: ?>>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile %0 [5, 6] {interchange = [1, 0]} : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation)
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile %0 [5, 6] {interchange = [1, 0]} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // CHECK-LABEL: func @matvec_perm
@@ -187,11 +187,11 @@ func.func @matmul_perm(%A: memref<?x?xf32, strided<[?, 1], offset: ?>>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:3 = transform.structured.tile %0 [2000, 3000, 4000] {interchange = [1, 2, 0]} : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-    %2, %loops_2:3 = transform.structured.tile %1 [200, 300, 400] {interchange = [1, 0, 2]} : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-    %3, %loops_3:3 = transform.structured.tile %2 [20, 30, 40] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:3 = transform.structured.tile %0 [2000, 3000, 4000] {interchange = [1, 2, 0]} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+    %2, %loops_2:3 = transform.structured.tile %1 [200, 300, 400] {interchange = [1, 0, 2]} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+    %3, %loops_3:3 = transform.structured.tile %2 [20, 30, 40] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // CHECK-LABEL: func @matmul_perm
diff --git a/mlir/test/Dialect/Linalg/transform-promotion.mlir b/mlir/test/Dialect/Linalg/transform-promotion.mlir
index b29a6cbdc28c..2f98e394fe05 100644
--- a/mlir/test/Dialect/Linalg/transform-promotion.mlir
+++ b/mlir/test/Dialect/Linalg/transform-promotion.mlir
@@ -59,9 +59,9 @@ func.func @promote_subview_matmul(%arg0: memref<?x?xf32, strided<[?, 1], offset:
 // CHECK-SAME:                outs(%[[v2]] : memref<?x?xf32>)
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = transform.structured.promote %0 { operands_to_promote = [0, 1, 2], use_full_tiles_by_default }
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = transform.structured.promote %0 { operands_to_promote = [0, 1, 2], use_full_tiles_by_default } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -119,11 +119,11 @@ func.func @promote_first_subview_matmul(%arg0: memref<?x?xf32, strided<[?, 1], o
 // CHECK-SAME:          outs(%[[s2]] : memref<?x?xf32, strided<[?, ?], offset: ?>>)
 
 transform.with_pdl_patterns {
-^bb0(%arg0: !pdl.operation):
-  sequence %arg0 : !pdl.operation failures(propagate) {
-    ^bb0(%arg1: !pdl.operation):
-      %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-      %1 = transform.structured.promote %0 { operands_to_promote = [0], use_full_tiles_by_default }
+^bb0(%arg0: !transform.any_op):
+  sequence %arg0 : !transform.any_op failures(propagate) {
+    ^bb0(%arg1: !transform.any_op):
+      %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+      %1 = transform.structured.promote %0 { operands_to_promote = [0], use_full_tiles_by_default } : (!transform.any_op) -> !transform.any_op
   }
 }
 
@@ -136,13 +136,13 @@ func.func @aligned_promote_fill(%arg0: memref<?x?xf32, strided<[?, 1], offset: ?
   %c1 = arith.constant 1 : index
   %cf = arith.constant 1.0 : f32
   %3 = memref.subview %arg0[%c0, %c0][%c2000, %c4000][%c1, %c1] :
- 	 memref<?x?xf32, strided<[?, 1], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
+         memref<?x?xf32, strided<[?, 1], offset: ?>> to memref<?x?xf32, strided<[?, ?], offset: ?>>
   linalg.fill
    ins(%cf : f32) outs(%3 : memref<?x?xf32, strided<[?, ?], offset: ?>>)
   return
 }
 // CHECK-LABEL: func @aligned_promote_fill
-// CHECK:	  %[[cf:.*]] = arith.constant 1.{{.*}} : f32
+// CHECK:         %[[cf:.*]] = arith.constant 1.{{.*}} : f32
 // CHECK:         %[[s0:.*]] = memref.subview {{.*}}: memref<?x?xf32, strided{{.*}}> to memref<?x?xf32, strided{{.*}}>
 // CHECK:         %[[a0:.*]] = memref.alloc() {alignment = 32 : i64} : memref<32000000xi8>
 // CHECK:         %[[v0:.*]] = memref.view %[[a0]]{{.*}} : memref<32000000xi8> to memref<?x?xf32>
@@ -152,11 +152,11 @@ func.func @aligned_promote_fill(%arg0: memref<?x?xf32, strided<[?, 1], offset: ?
 // CHECK:         linalg.fill ins(%[[cf]] : f32) outs(%[[v0]] : memref<?x?xf32>)
 
 transform.with_pdl_patterns {
-^bb0(%arg0: !pdl.operation):
-  sequence %arg0 : !pdl.operation failures(propagate) {
-    ^bb0(%arg1: !pdl.operation):
-      %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-      %1 = transform.structured.promote %0 { operands_to_promote = [1], use_full_tile_buffers = [false, true], alignment = 32}
+^bb0(%arg0: !transform.any_op):
+  sequence %arg0 : !transform.any_op failures(propagate) {
+    ^bb0(%arg1: !transform.any_op):
+      %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+      %1 = transform.structured.promote %0 { operands_to_promote = [1], use_full_tile_buffers = [false, true], alignment = 32} : (!transform.any_op) -> !transform.any_op
   }
 }
 
@@ -170,13 +170,13 @@ func.func @aligned_promote_fill_complex(%arg0: memref<?x?xcomplex<f32>, strided<
   %cf = arith.constant 1.0 : f32
   %cc = complex.create %cf, %cf : complex<f32>
   %3 = memref.subview %arg0[%c0, %c0][%c2000, %c4000][%c1, %c1] :
- 	 memref<?x?xcomplex<f32>, strided<[?, 1], offset: ?>> to memref<?x?xcomplex<f32>, strided<[?, ?], offset: ?>>
+         memref<?x?xcomplex<f32>, strided<[?, 1], offset: ?>> to memref<?x?xcomplex<f32>, strided<[?, ?], offset: ?>>
   linalg.fill ins(%cc : complex<f32>)
              outs(%3 : memref<?x?xcomplex<f32>, strided<[?, ?], offset: ?>>)
   return
 }
 // CHECK-LABEL: func @aligned_promote_fill_complex
-// CHECK:	  %[[cc:.*]] = complex.create {{.*}} : complex<f32>
+// CHECK:         %[[cc:.*]] = complex.create {{.*}} : complex<f32>
 // CHECK:         %[[s0:.*]] = memref.subview {{.*}}: memref<?x?xcomplex<f32>, strided{{.*}}> to memref<?x?xcomplex<f32>, strided{{.*}}>
 // CHECK:         %[[a0:.*]] = memref.alloc() {alignment = 32 : i64} : memref<64000000xi8>
 // CHECK:         %[[v0:.*]] = memref.view %[[a0]]{{.*}} : memref<64000000xi8> to memref<?x?xcomplex<f32>>
@@ -186,10 +186,10 @@ func.func @aligned_promote_fill_complex(%arg0: memref<?x?xcomplex<f32>, strided<
 // CHECK:         linalg.fill ins(%[[cc]] : complex<f32>) outs(%[[v0]] : memref<?x?xcomplex<f32>>)
 
 transform.with_pdl_patterns {
-^bb0(%arg0: !pdl.operation):
-  sequence %arg0 : !pdl.operation failures(propagate) {
-    ^bb0(%arg1: !pdl.operation):
-      %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-      %1 = transform.structured.promote %0 { operands_to_promote = [1], use_full_tile_buffers = [false, true], alignment = 32}
+^bb0(%arg0: !transform.any_op):
+  sequence %arg0 : !transform.any_op failures(propagate) {
+    ^bb0(%arg1: !transform.any_op):
+      %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+      %1 = transform.structured.promote %0 { operands_to_promote = [1], use_full_tile_buffers = [false, true], alignment = 32} : (!transform.any_op) -> !transform.any_op
   }
 }
diff --git a/mlir/test/Dialect/Linalg/transform-tile-and-fuse.mlir b/mlir/test/Dialect/Linalg/transform-tile-and-fuse.mlir
index 032b480f5041..3ef94a075bef 100644
--- a/mlir/test/Dialect/Linalg/transform-tile-and-fuse.mlir
+++ b/mlir/test/Dialect/Linalg/transform-tile-and-fuse.mlir
@@ -41,16 +41,18 @@ module {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
+  ^bb1(%arg1: !transform.any_op):
     // Find the root and all producers.
-    %root = transform.structured.match attributes{"__root__"} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %producers = transform.structured.match attributes{"__producer__"} in %arg1 : (!pdl.operation) -> !pdl.operation
+    %root = transform.structured.match attributes{"__root__"} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %producers = transform.structured.match attributes{"__producer__"} in %arg1 : (!transform.any_op) -> !transform.any_op
 
     // Tile the root.
     %forall_op, %tiled_op = transform.structured.tile_to_forall_op %root num_threads [10, 20]
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     // Fuse all producers.
     transform.structured.fuse_into_containing_op %producers into %forall_op
+      : (!transform.any_op, !transform.any_op) -> !transform.any_op
   }
 }
 
@@ -98,16 +100,18 @@ module {
   }
 
   transform.sequence failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
+  ^bb1(%arg1: !transform.any_op):
     // Find the root and all producers.
-    %root = transform.structured.match attributes{"__root__"} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %producers = transform.structured.match attributes{"__producer__"} in %arg1 : (!pdl.operation) -> !pdl.operation
+    %root = transform.structured.match attributes{"__root__"} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %producers = transform.structured.match attributes{"__producer__"} in %arg1 : (!transform.any_op) -> !pdl.operation
     %reversed_producers = transform.test_reverse_payload_ops %producers
 
     // Tile the root.
     %forall_op, %tiled_op = transform.structured.tile_to_forall_op %root num_threads [10, 20]
+         : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
     // Fuse all producers.
     transform.structured.fuse_into_containing_op %reversed_producers into %forall_op
+      : (!pdl.operation, !transform.any_op) -> !transform.any_op
   }
 }
diff --git a/mlir/test/Dialect/Linalg/transform-tile-reduction.mlir b/mlir/test/Dialect/Linalg/transform-tile-reduction.mlir
index 16578bdb305a..f281add1c28a 100644
--- a/mlir/test/Dialect/Linalg/transform-tile-reduction.mlir
+++ b/mlir/test/Dialect/Linalg/transform-tile-reduction.mlir
@@ -15,10 +15,10 @@ func.func @reduction_tile(%arg0: tensor<?x?xf32>, %out: tensor<?xf32>) -> tensor
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %loop, %1, %2, %3 = transform.structured.tile_reduction_using_scf %0
-    by tile_sizes = [0, 5]
+    by tile_sizes = [0, 5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
@@ -70,10 +70,10 @@ func.func @reduction_tile_transpose(%arg0: tensor<?x?xf32>, %out: tensor<?xf32>)
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %loop, %1, %2, %3 = transform.structured.tile_reduction_using_scf %0 
-    by tile_sizes = [5, 0]
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %loop, %1, %2, %3 = transform.structured.tile_reduction_using_scf %0
+    by tile_sizes = [5, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 //     CHECK: func @reduction_tile_transpose
@@ -107,10 +107,10 @@ func.func @reduction_tile_parallel(
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %loop, %1, %2, %3 = transform.structured.tile_reduction_using_forall %0
-    by num_threads = [0, 5], tile_sizes = []
+    by num_threads = [0, 5], tile_sizes = [] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0)[s0] -> (-(d0 * (s0 ceildiv 5)) + s0, s0 ceildiv 5)>
@@ -159,10 +159,10 @@ func.func @matmul_tile_parallel(
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   %loop, %1, %2, %3 = transform.structured.tile_reduction_using_forall %0
-    by num_threads = [0, 0, 5], tile_sizes = []
+    by num_threads = [0, 0, 5], tile_sizes = [] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0)[s0] -> (-(d0 * (s0 ceildiv 5)) + s0, s0 ceildiv 5)>
@@ -218,10 +218,10 @@ func.func @reduction_tile_parallel_cyclic_dist(
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %loop, %1, %2, %3 = transform.structured.tile_reduction_using_forall %0 
-    by num_threads = [0, 5], tile_sizes = [0, 3], mapping = [#gpu.thread<x>]
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %loop, %1, %2, %3 = transform.structured.tile_reduction_using_forall %0
+    by num_threads = [0, 5], tile_sizes = [0, 3], mapping = [#gpu.thread<x>] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // CHECK-DAG: #[[MAP0:.*]] = affine_map<()[s0] -> (s0 * 3)>
@@ -283,22 +283,22 @@ func.func @reduction_tile_parallel_cyclic_dist(
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %loop, %1, %2, %3 = transform.structured.tile_reduction_using_forall %0 
-    by num_threads = [0, 5], tile_sizes = [0, 3], mapping = [#gpu.thread<x>]
-  
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %loop, %1, %2, %3 = transform.structured.tile_reduction_using_forall %0
+    by num_threads = [0, 5], tile_sizes = [0, 3], mapping = [#gpu.thread<x>] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+
   //      CHECK:     expecting fill
   // CHECK-NEXT:     linalg.fill
-  transform.print %1 {name = "expecting fill"} : !pdl.operation
+  transform.print %1 {name = "expecting fill"} : !transform.any_op
   //      CHECK:     expecting parallel reduction
   // CHECK-NEXT:     linalg.generic
   //      CHECK:     iterator_types = ["parallel", "reduction"]
-  transform.print %2 {name = "expecting parallel reduction"} : !pdl.operation
+  transform.print %2 {name = "expecting parallel reduction"} : !transform.any_op
   //      CHECK:     expecting parallel reduction
   // CHECK-NEXT:     linalg.generic
   //      CHECK:     iterator_types = ["parallel", "reduction"]
-  transform.print %3 {name = "expecting parallel reduction"} : !pdl.operation
+  transform.print %3 {name = "expecting parallel reduction"} : !transform.any_op
 }
 
 // -----
@@ -320,11 +320,11 @@ func.func @reduction_untiled_forall(
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
+^bb0(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
   // expected-error @below {{could not tile reduction}}
   %loop, %1, %2, %3 = transform.structured.tile_reduction_using_forall %0
-    by num_threads = [5], tile_sizes = [3], mapping = [#gpu.thread<x>]
+    by num_threads = [5], tile_sizes = [3], mapping = [#gpu.thread<x>] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 
 }
 
@@ -346,9 +346,9 @@ module {
     return %0 : tensor<?xf32>
   }
   transform.sequence failures(propagate) {
-  ^bb0(%arg0: !pdl.operation):
-    %0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!pdl.operation) -> !pdl.operation
+  ^bb0(%arg0: !transform.any_op):
+    %0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
     // expected-error @below {{transform.structured.tile_reduction_using_scf failed to apply}}
-    %for_op, %fill_op, %split_linalg_op, %combining_linalg_op = transform.structured.tile_reduction_using_scf %0 by tile_sizes = [0, 5]
+    %for_op, %fill_op, %split_linalg_op, %combining_linalg_op = transform.structured.tile_reduction_using_scf %0 by tile_sizes = [0, 5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
   }
 }
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index 4712093a81e4..489b76b37598 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -11,10 +11,10 @@ func.func @contraction_dot(%A: memref<1584xf32>, %B: memref<1584xf32>, %C: memre
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.dot"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.dot"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -30,10 +30,10 @@ func.func @contraction_matvec(%A: memref<1584x1584xf32>, %B: memref<1584xf32>, %
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -48,10 +48,10 @@ func.func @contraction_matmul(%A: memref<1584x1584xf32>, %B: memref<1584x1584xf3
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -67,10 +67,10 @@ func.func @contraction_batch_matmul(%A: memref<1584x1584x1584xf32>, %B: memref<1
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.batch_matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.batch_matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -107,10 +107,10 @@ func.func @vectorization_test(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -147,10 +147,10 @@ func.func @generic_output_transpose(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -174,10 +174,10 @@ func.func @generic_interchanged_transpose(%arg0: tensor<12x128x32xf32>) -> tenso
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -214,10 +214,10 @@ func.func @vectorization_test_integer(%A: memref<8x16xi32>, %B: memref<16x32xi32
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -234,10 +234,10 @@ func.func @vectorization_test_2(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -258,10 +258,10 @@ func.func @test_vectorize_scalar_input(%A : memref<8x16xf32>, %arg0 : f32) {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -282,10 +282,10 @@ func.func @test_do_not_vectorize_unsupported_element_types(%A : memref<8x16xcomp
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -327,10 +327,10 @@ func.func @vectorize_affine_apply(%arg0: tensor<5xf32>, %arg3: index) -> tensor<
 // CHECK:   vector.transfer_write %[[CAST]], %[[EMPTY]][%[[C0:.*]]] {in_bounds = [true]} : vector<5xi32>, tensor<5xi32>
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -344,10 +344,10 @@ func.func @test_vectorize_fill(%A : memref<8x16xf32>, %arg0 : f32) {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -362,10 +362,10 @@ func.func @test_vectorize_fill_scalar(%A : memref<f32>, %arg0 : f32) {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -379,10 +379,10 @@ func.func @test_vectorize_copy(%A : memref<8x16xf32>, %B : memref<8x16xf32>) {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -399,10 +399,10 @@ func.func @test_vectorize_copy_scalar(%A : memref<f32>, %B : memref<f32>) {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -415,10 +415,10 @@ func.func @test_vectorize_copy_complex(%A : memref<8x16xcomplex<f32>>, %B : memr
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -443,10 +443,10 @@ func.func @test_vectorize_trailing_index(%arg0: memref<1x2x4x8xindex>) {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -472,10 +472,10 @@ func.func @test_vectorize_inner_index(%arg0: memref<1x2x4x8xindex>) {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -557,10 +557,10 @@ func.func @generic_vectorize(%arg0: memref<4x256xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -648,10 +648,10 @@ func.func @generic_vectorize_tensor(%arg0: tensor<4x256xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1 { disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -692,10 +692,10 @@ func.func @generic_vectorize_broadcast_transpose(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -735,10 +735,10 @@ func.func @vectorization_transpose(%A: memref<14x7xf32>, %B: memref<16x14xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -767,10 +767,10 @@ func.func @matmul_tensors(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -796,10 +796,10 @@ func.func @pad_static(%arg0: tensor<2x?x2xf32>, %pad_value: f32) -> tensor<2x3x4
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1 { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -825,10 +825,10 @@ func.func @pad_static_source(%arg0: tensor<2x5x2xf32>, %pad_value: f32) -> tenso
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 
@@ -862,10 +862,10 @@ func.func @pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: in
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -882,10 +882,10 @@ func.func @pad_static_complex(%arg0: tensor<2x5x2xcomplex<f32>>, %pad_value: com
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -912,10 +912,10 @@ func.func @pad_and_transfer_read(%arg0: tensor<5x6xf32>) -> vector<7x9xf32> {
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1 { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -945,10 +945,10 @@ func.func @pad_and_transfer_write_static(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4  { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4  { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 
@@ -982,10 +982,10 @@ func.func @pad_and_transfer_write_dynamic_static(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4 { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 
@@ -1016,10 +1016,10 @@ func.func @pad_and_insert_slice_source(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4  { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4  { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 
@@ -1044,10 +1044,10 @@ func.func @pad_and_insert_slice_dest(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1081,10 +1081,10 @@ func.func @pad_tensor_non_const_pad_value(%arg0: tensor<5x6xf32>) -> tensor<12x1
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4  { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4  { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1116,10 +1116,10 @@ func.func @sum_exp(%input: tensor<4x16x8xf32>, %output: tensor<4x16xf32>)
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1161,10 +1161,10 @@ func.func @sum_exp_2(%input: tensor<3x2xf32>, %input_2: tensor<5x4xf32>, %output
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1191,10 +1191,10 @@ func.func @red_max_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4 { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1222,10 +1222,10 @@ func.func @red_min_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1252,10 +1252,10 @@ func.func @red_mul_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1282,10 +1282,10 @@ func.func @red_or_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1312,10 +1312,10 @@ func.func @red_and_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1342,10 +1342,10 @@ func.func @red_xor_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1376,10 +1376,10 @@ func.func @explicit_broadcast(%arg0: tensor<4x4xf32>, %arg1: tensor<4x1xf32>) ->
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4
+^bb1(%arg1: !transform.any_op):
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1414,14 +1414,14 @@ func.func @fused_broadcast_red_2d(%arg0: tensor<4x4xf32>, %arg1: tensor<4x1xf32>
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 
-  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %4 = get_closest_isolated_parent %3 : (!pdl.operation) -> !pdl.operation
-  %5 = transform.structured.vectorize %4
+  %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %4 = get_closest_isolated_parent %3 : (!transform.any_op) -> !transform.any_op
+  %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1461,10 +1461,10 @@ func.func @reduce_1d(%arg0: tensor<32xf32>) -> tensor<f32> {
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 
@@ -1492,10 +1492,10 @@ func.func @not_projected_permutation(%arg0: tensor<8x8xf32>) -> tensor<6x6x3x3xf
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1531,10 +1531,10 @@ func.func @mixed_parallel_reduced_results(%arg0 : tensor<2x4x8xf32>,
 //   CHECK-DAG:   vector.transfer_write %[[ADD]], %[[ARG3]]
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1567,10 +1567,10 @@ func.func @vectorize_1d_tensor_extract(%arg0: tensor<3xf32>, %arg1: tensor<4x3xi
 // CHECK: vector.transfer_write %[[GATHER]]
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -1603,10 +1603,10 @@ func.func @vectorize_nd_tensor_extract_constant_idx(%arg0: tensor<3x3xf32>, %arg
 // CHECK:  }
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
  }
 
 // -----
@@ -1642,10 +1642,10 @@ func.func @vectorize_nd_tensor_extract_transfer_read_basic(%arg0: tensor<3x3x3xf
 // CHECK:   vector.transfer_write %[[READ]], %[[ARG1]][%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x1x3xf32>, tensor<1x1x3xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
 }
 
  // -----
@@ -1692,10 +1692,10 @@ func.func @vectorize_nd_tensor_extract_transfer_read_complex(%6: tensor<45x80x16
 // CHECK:           %[[VAL_22:.*]] = vector.transfer_write %[[VAL_21]], %[[VAL_5]]{{\[}}%[[VAL_9]], %[[VAL_9]]] {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x4xf32>
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
  }
 
 // -----
@@ -1740,10 +1740,10 @@ func.func @vectorize_nd_tensor_extract_index_from_tensor(%arg0: tensor<3x3xf32>,
 // CHECK:    vector.transfer_write %[[GATHER]], %[[ARG4]][%[[C0]], %[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true, true]} : vector<4x7x3x2xf32>, tensor<4x7x3x2xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
 }
 // -----
 
@@ -1784,10 +1784,10 @@ func.func @vectorize_nd_tensor_extract_contiguous_and_gather(%arg0: tensor<6xf32
 // CHECK:           return %[[VAL_14]] : tensor<5xf32>
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
  }
 
 // -----
@@ -1826,10 +1826,10 @@ func.func @vectorize_nd_tensor_extract_with_affine_apply_contiguous(%6: tensor<8
 // CHECK:         }
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
  }
 
 // -----
@@ -1896,10 +1896,10 @@ func.func @vectorize_nd_tensor_extract_with_tensor_extract(%input_1: tensor<1x20
 // CHECK:         }
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
  }
 
 // -----
@@ -1930,9 +1930,9 @@ func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguou
 // CHECK:           %[[VAL_22:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x3xf32> } : vector<1x4xi1> -> tensor<1x3xf32>
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   transform.structured.masked_vectorize %0 vector_sizes [1, 4] { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   transform.structured.masked_vectorize %0 vector_sizes [1, 4] { vectorize_nd_extract } : !transform.any_op
  }
 
  // -----
@@ -1985,9 +1985,9 @@ func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguo
 // CHECK:         }
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   transform.structured.masked_vectorize %0 vector_sizes [1, 4] { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   transform.structured.masked_vectorize %0 vector_sizes [1, 4] { vectorize_nd_extract } : !transform.any_op
 }
 
 // -----
@@ -2028,10 +2028,10 @@ func.func @vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tensor<80x16
 // CHECK:         }
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
  }
 
 // -----
@@ -2067,9 +2067,9 @@ func.func @masked_vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tenso
 // CHECK:           %[[VAL_25:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x3xf32> } : vector<1x4xi1> -> tensor<1x3xf32>
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   transform.structured.masked_vectorize %0 vector_sizes [1, 4] { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   transform.structured.masked_vectorize %0 vector_sizes [1, 4] { vectorize_nd_extract } : !transform.any_op
  }
 
  // -----
@@ -2122,9 +2122,9 @@ func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_gather(%
 // CHECK:         }
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   transform.structured.masked_vectorize %0 vector_sizes [1, 4] { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   transform.structured.masked_vectorize %0 vector_sizes [1, 4] { vectorize_nd_extract } : !transform.any_op
  }
 
 // -----
@@ -2161,10 +2161,10 @@ func.func @vectorize_nd_tensor_extract_with_maxsi_gather(%arg0: tensor<80x16xf32
 // CHECK:         }
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
  }
 
 // -----
@@ -2201,10 +2201,10 @@ func.func @vectorize_nd_tensor_extract_with_maxsi_contiguous(%arg0: tensor<80x16
 // CHECK:         }
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
  }
 
 // -----
@@ -2240,10 +2240,10 @@ func.func @vectorize_nd_tensor_extract_block_arg(%arg0: tensor<5x6xf32>, %arg1:
 // CHECK:         }
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-   %2 = transform.structured.vectorize %1 { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+   %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
  }
 
 
@@ -2265,10 +2265,10 @@ func.func @vectorize_map(%arg0: memref<64xf32>,
 // CHECK-NEXT:    arith.addf %[[LHS]], %[[RHS]] : vector<64xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.map"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.map"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -2284,10 +2284,10 @@ func.func @vectorize_transpose(%arg0: memref<16x32x64xf32>,
 // CHECK-SAME:      [1, 2, 0] : vector<16x32x64xf32> to vector<32x64x16xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.transpose"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.transpose"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -2307,10 +2307,10 @@ func.func @vectorize_reduce(%arg0: memref<16x32x64xf32>,
 // CHECK-SAME:    : vector<16x32x64xf32> to vector<16x64xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.reduce"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.reduce"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -2342,9 +2342,9 @@ func.func @vectorize_dynamic_identity(%arg0: tensor<?xf32>,
 // CHECK:           %[[VAL_14:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [4]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [4] : !transform.any_op
 }
 
 // -----
@@ -2376,9 +2376,9 @@ func.func @vectorize_dynamic_1d_broadcast(%arg0: tensor<?xf32>,
 // CHECK:           %[[VAL_14:.*]] = vector.mask %{{.*}} { vector.transfer_write %[[VAL_13]], {{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [4]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [4] : !transform.any_op
 }
 
 // -----
@@ -2414,9 +2414,9 @@ func.func @vectorize_dynamic_2d_transpose(%arg0: tensor<?x?xf32>,
 // CHECK:           %[[VAL_17:.*]] = vector.mask %[[VAL_12]] { vector.transfer_write %[[VAL_16]], %{{.*}} {in_bounds = [true, true]} : vector<4x8xf32>, tensor<?x?xf32> } : vector<4x8xi1> -> tensor<?x?xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [4, 8]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [4, 8] : !transform.any_op
 }
 
 // -----
@@ -2451,9 +2451,9 @@ func.func @vectorize_dynamic_generic_2d_broadcast(%arg0: tensor<?x?xf32>,
 // CHECK:           %[[VAL_18:.*]] = vector.mask %[[VAL_12]] { vector.transfer_write %{{.*}} {in_bounds = [true, true]} : vector<4x8xf32>, tensor<?x?xf32> } : vector<4x8xi1> -> tensor<?x?xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [4, 8]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [4, 8] : !transform.any_op
 }
 
 // -----
@@ -2473,9 +2473,9 @@ func.func @vectorize_dynamic_reduction(%arg0: tensor<?x?xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [4, 8]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [4, 8] : !transform.any_op
 }
 
 // CHECK-LABEL:   @vectorize_dynamic_reduction(
@@ -2511,9 +2511,9 @@ func.func @vectorize_dynamic_transpose_reduction(%arg0: tensor<?x?x?xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [4, 8, 16]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [4, 8, 16] : !transform.any_op
 }
 
 // CHECK-LABEL:   @vectorize_dynamic_transpose_reduction(
@@ -2535,7 +2535,7 @@ transform.sequence failures(propagate) {
 // -----
 
 // This is a regression test. This IR cannot be vectorized, but
-// structured.vectorize should nevertheless succeed.
+// structured.vectorize should nevertheless succeed. : (!transform.any_op) -> !transform.any_op
 
 #map = affine_map<(d0) -> (d0)>
 // CHECK-LABEL:   @not_vectorizable
@@ -2559,9 +2559,9 @@ func.func @not_vectorizable(%arg0: tensor<1x?xf32>, %arg1: index, %arg2: index,
   return %1 : tensor<1x128xf32>
 }
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
-  %0 = transform.structured.match ops{["func.func"]} in %arg0 : (!pdl.operation) -> !pdl.operation
-  %1 = transform.structured.vectorize %0
+^bb0(%arg0: !transform.any_op):
+  %0 = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+  %1 = transform.structured.vectorize %0 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -2593,10 +2593,10 @@ func.func @wrong_reduction_detection(%input: tensor<120x64xf32>) -> tensor<120x6
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // CHECK-LABEL: @wrong_reduction_detection
@@ -2605,7 +2605,7 @@ transform.sequence failures(propagate) {
 
 // -----
 
-// Don't vectorize tensor<0xf32>
+// Don't vectorize tensor<0xf32> : (!transform.any_op) -> !transform.any_op
 // CHECK-LABEL: @tensor_size0
 // CHECK:         linalg.generic
 func.func @tensor_size0(%arg0: tensor<0xf32>,
@@ -2622,10 +2622,10 @@ func.func @tensor_size0(%arg0: tensor<0xf32>,
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -2665,9 +2665,9 @@ func.func @vectorize_partial_dynamic_identity(%arg0: tensor<8x?xf32>,
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [8, 32]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [8, 32] : !transform.any_op
 }
 
 // -----
@@ -2715,9 +2715,9 @@ func.func @extract_masked_vectorize(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf3
 // CHECK:           %[[VAL_24:.*]] = vector.mask %[[VAL_10]] { vector.transfer_write %[[VAL_22]], %[[VAL_1]]{{\[}}%[[VAL_23]], %[[VAL_23]]] {in_bounds = [true, true]} : vector<3x3xf32>, tensor<?x?xf32> } : vector<3x3xi1> -> tensor<?x?xf32>
 
 transform.sequence failures(propagate) {
- ^bb1(%arg1: !pdl.operation):
-   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-   transform.structured.masked_vectorize %0 vector_sizes [3, 3] { vectorize_nd_extract }
+ ^bb1(%arg1: !transform.any_op):
+   %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+   transform.structured.masked_vectorize %0 vector_sizes [3, 3] { vectorize_nd_extract } : !transform.any_op
  }
 
 // -----
@@ -2742,9 +2742,9 @@ func.func @do_not_generate_masks(%arg0: tensor<8x32xf32>,
 // CHECK-NOT: vector.mask
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [8, 32]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [8, 32] : !transform.any_op
 }
 
 // -----
@@ -2782,9 +2782,9 @@ func.func @vectorize_static_shape_with_mask(%arg0: tensor<8x30xf32>,
 // CHECK:           %[[VAL_15:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %[[VAL_13]], %[[VAL_2]][%[[VAL_14]], %[[VAL_14]]] {in_bounds = [true, true]} : vector<8x32xf32>, tensor<8x30xf32> } : vector<8x32xi1> -> tensor<8x30xf32>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [8, 32]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [8, 32] : !transform.any_op
 }
 
 // -----
@@ -2802,9 +2802,9 @@ func.func @vectorize_dynamic_fill(%A : tensor<?x?xf32>, %arg0 : f32) -> tensor<?
 //   CHECK: vector.mask %[[MASK]] { vector.transfer_write %[[BCAST]], {{.*}} {in_bounds = [true, true]} : vector<8x16xf32>, tensor<?x?xf32> } : vector<8x16xi1>
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [8, 16]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [8, 16] : !transform.any_op
 }
 
 // -----
@@ -2823,9 +2823,9 @@ func.func @test_masked_vectorize_linalg_copy(%A : memref<?x?xf32>, %B : memref<?
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.copy"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [2, 4]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [2, 4] : !transform.any_op
 }
 
 // -----
@@ -2858,10 +2858,10 @@ func.func @test_masked_vectorize_pad(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
+^bb1(%arg1: !transform.any_op):
   %0 = transform.structured.match ops{["tensor.pad"]} in %arg1
-    : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [2, 4]
+    : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [2, 4] : !transform.any_op
 }
 
 // -----
@@ -2879,10 +2879,10 @@ func.func @test_masked_pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: inde
 
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-  %2 = transform.structured.vectorize %1  { vectorize_padding }
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+  %2 = transform.structured.vectorize %1  { vectorize_padding } : (!transform.any_op) -> !transform.any_op
 }
 
 // -----
@@ -2920,7 +2920,7 @@ func.func @vectorize_dynamic_matmul(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C
 // CHECK:         }
 
 transform.sequence failures(propagate) {
-^bb1(%arg1: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.masked_vectorize %0 vector_sizes [8, 16, 4]
+^bb1(%arg1: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.masked_vectorize %0 vector_sizes [8, 16, 4] : !transform.any_op
 }
diff --git a/mlir/test/Dialect/SCF/transform-op-take-assumed-branch.mlir b/mlir/test/Dialect/SCF/transform-op-take-assumed-branch.mlir
index 68325702ac28..70876b7184bd 100644
--- a/mlir/test/Dialect/SCF/transform-op-take-assumed-branch.mlir
+++ b/mlir/test/Dialect/SCF/transform-op-take-assumed-branch.mlir
@@ -10,10 +10,10 @@ func.func @if_no_else(%cond: i1, %a: index, %b: memref<?xf32>, %c: i8) {
 
 transform.sequence failures(propagate) {
 ^bb0(%arg1: !transform.any_op):
-  %if = transform.structured.match ops{["scf.if"]} in %arg1 
+  %if = transform.structured.match ops{["scf.if"]} in %arg1
     : (!transform.any_op) -> !transform.any_op
   // expected-error @+1 {{requires an scf.if op with a single-block `else` region}}
-  transform.scf.take_assumed_branch %if take_else_branch 
+  transform.scf.take_assumed_branch %if take_else_branch
     : (!transform.any_op) -> ()
 }
 
@@ -63,8 +63,9 @@ func.func @tile_tensor_pad(
 transform.sequence failures(propagate) {
 ^bb0(%arg1: !transform.any_op):
   %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 
-    : (!transform.any_op) -> !pdl.operation
-  transform.structured.tile_to_forall_op %0 tile_sizes[1, 1]
+    : (!transform.any_op) -> !transform.any_op
+  transform.structured.tile_to_forall_op %0 tile_sizes[1, 1] 
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 
   %if = transform.structured.match ops{["scf.if"]} in %arg1 
     : (!transform.any_op) -> !transform.any_op
diff --git a/mlir/test/Dialect/Tensor/tiling.mlir b/mlir/test/Dialect/Tensor/tiling.mlir
index 3f07e3ce6712..4286a5464322 100644
--- a/mlir/test/Dialect/Tensor/tiling.mlir
+++ b/mlir/test/Dialect/Tensor/tiling.mlir
@@ -32,9 +32,9 @@ func.func @dynamic_pad_tensor_3_4(%input_tensor: tensor<?x?xf32>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 3]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -69,9 +69,9 @@ func.func @dynamic_pad_tensor_0_3(%input_tensor: tensor<?x?xf32>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loop = transform.structured.tile_to_scf_for %0 [0, 3]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loop = transform.structured.tile_to_scf_for %0 [0, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -103,9 +103,9 @@ func.func @static_pad_tensor_3_4(%input_tensor: tensor<7x9xf32>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 3]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -135,9 +135,9 @@ func.func @static_pad_tensor_0_3(%input_tensor: tensor<7x9xf32>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loop = transform.structured.tile_to_scf_for %0 [0, 3]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loop = transform.structured.tile_to_scf_for %0 [0, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -173,9 +173,9 @@ func.func @static_pad_tile_evenly_0_3(%input_tensor: tensor<7x9xf32>,
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loop = transform.structured.tile_to_scf_for %0 [0, 3]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loop = transform.structured.tile_to_scf_for %0 [0, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -209,9 +209,9 @@ func.func @NC_to_NCnc(%arg0: tensor<128x256xf32>, %arg1: tensor<4x8x32x32xf32>)
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -237,9 +237,9 @@ func.func @KC_to_CKkc(%arg0: tensor<128x256xf32>, %arg1: tensor<32x4x32x8xf32>)
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -272,9 +272,9 @@ func.func @pad_and_pack_static(%input: tensor<13x15xf32>, %output: tensor<2x8x8x
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -321,9 +321,9 @@ func.func @pad_and_pack_partially_dynamic(%input: tensor<?x?xf32>, %output: tens
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -375,9 +375,9 @@ func.func @pad_and_pack_fully_dynamic(%source: tensor<?x?xf32>, %dest: tensor<?x
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -422,9 +422,9 @@ func.func @NCnc_to_NC(%source: tensor<8x8x32x16xf32>, %dest: tensor<256x128xf32>
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -468,9 +468,9 @@ func.func @CKkc_to_KC(%source: tensor<32x4x32x8xf32>, %dest: tensor<128x256xf32>
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -504,9 +504,9 @@ func.func @perfect_CKkc_to_KC(%source: tensor<32x4x2x4xf32>, %dest: tensor<8x128
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -546,9 +546,9 @@ func.func @dynamic_perfect_CKkc_to_KC(%source: tensor<?x?x2x2xf32>, %dest: tenso
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [2, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -581,9 +581,9 @@ func.func @perfect_NKPQk_to_NPQK(%source: tensor<1x4x6x6x2xf32>, %dest: tensor<1
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 1, 4]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 1, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -607,9 +607,9 @@ func.func @fully_dynamic_unpack(%source: tensor<?x?x?x?xf32>, %dest: tensor<?x?x
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [4, 8]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:2 = transform.structured.tile_to_scf_for %0 [4, 8] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
 }
 
 // -----
@@ -641,7 +641,7 @@ func.func @perfect_NPQK_to_NKPQk(%source: tensor<1x6x6x8xf32>, %dest: tensor<1x4
 }
 
 transform.sequence failures(propagate) {
-  ^bb0(%arg1: !pdl.operation):
-    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 1, 1]
+  ^bb0(%arg1: !transform.any_op):
+    %0 = transform.structured.match ops{["tensor.pack"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 1, 1] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
 }
diff --git a/mlir/test/Dialect/Transform/selective-targeting.mlir b/mlir/test/Dialect/Transform/selective-targeting.mlir
index 231ff3099d17..13b0a1f40428 100644
--- a/mlir/test/Dialect/Transform/selective-targeting.mlir
+++ b/mlir/test/Dialect/Transform/selective-targeting.mlir
@@ -53,7 +53,7 @@ func.func @matmul_tensors_3(
 }
 
 transform.with_pdl_patterns {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   // Match matmul operations inside @matmul_tensors with test.attrA set.
   pdl.pattern @pdl_target_attrA : benefit(1) {
     %args = operands
@@ -74,13 +74,13 @@ transform.with_pdl_patterns {
     rewrite %0 with "transform.dialect"
   }
 
-  transform.sequence %arg0 : !pdl.operation failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = pdl_match @pdl_target_attrA in %arg1 : (!pdl.operation) -> !pdl.operation
-    transform.structured.tile %0 [4, 4, 4] : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-    %1 = pdl_match @pdl_target_attrC in %arg1 : (!pdl.operation) -> !pdl.operation
-    %2 = transform.get_closest_isolated_parent %1 : (!pdl.operation) -> !pdl.operation
-    transform.structured.vectorize %2
+  transform.sequence %arg0 : !transform.any_op failures(propagate) {
+  ^bb1(%arg1: !transform.any_op):
+    %0 = pdl_match @pdl_target_attrA in %arg1 : (!transform.any_op) -> !transform.any_op
+    transform.structured.tile %0 [4, 4, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+    %1 = pdl_match @pdl_target_attrC in %arg1 : (!transform.any_op) -> !transform.any_op
+    %2 = transform.get_closest_isolated_parent %1 : (!transform.any_op) -> !transform.any_op
+    transform.structured.vectorize %2 : (!transform.any_op) -> !transform.any_op
   }
 }
 
@@ -111,7 +111,7 @@ func.func @vectorize_none(
 }
 
 transform.with_pdl_patterns {
-^bb0(%arg0: !pdl.operation):
+^bb0(%arg0: !transform.any_op):
   pdl.pattern @pdl_target : benefit(1) {
     %args = operands
     %results = types
@@ -121,11 +121,11 @@ transform.with_pdl_patterns {
     rewrite %0 with "transform.dialect"
   }
 
-  transform.sequence %arg0 : !pdl.operation failures(propagate) {
-  ^bb1(%arg1: !pdl.operation):
-    %0 = pdl_match @pdl_target in %arg1 : (!pdl.operation) -> !pdl.operation
-    %1 = get_closest_isolated_parent %0 : (!pdl.operation) -> !pdl.operation
-    transform.structured.vectorize %1
+  transform.sequence %arg0 : !transform.any_op failures(propagate) {
+  ^bb1(%arg1: !transform.any_op):
+    %0 = pdl_match @pdl_target in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = get_closest_isolated_parent %0 : (!transform.any_op) -> !transform.any_op
+    transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
   }
 }
 
@@ -149,6 +149,6 @@ func.func @vectorize_all(
 }
 
 transform.sequence failures(propagate) {
-^bb0(%arg0: !pdl.operation):
-  transform.structured.vectorize %arg0
+^bb0(%arg0: !transform.any_op):
+  transform.structured.vectorize %arg0 : (!transform.any_op) -> !transform.any_op
 }
diff --git a/mlir/test/Dialect/Vector/transform-vector.mlir b/mlir/test/Dialect/Vector/transform-vector.mlir
index 2fbee4210970..beda04294b84 100644
--- a/mlir/test/Dialect/Vector/transform-vector.mlir
+++ b/mlir/test/Dialect/Vector/transform-vector.mlir
@@ -14,49 +14,49 @@ func.func @matmul_tensors(
 }
 
 transform.sequence failures(propagate) {
-^bb1(%module_op: !pdl.operation):
-  %0 = transform.structured.match ops{["linalg.matmul"]} in %module_op : (!pdl.operation) -> !pdl.operation
-  %1, %loops:3 = transform.structured.tile %0 [8, 4, 2] 
-    : (!pdl.operation) -> (!pdl.operation, !pdl.operation, !pdl.operation, !pdl.operation)
-  %2 = get_closest_isolated_parent %1 : (!pdl.operation) -> !pdl.operation
-  transform.structured.vectorize %2
+^bb1(%module_op: !transform.any_op):
+  %0 = transform.structured.match ops{["linalg.matmul"]} in %module_op : (!transform.any_op) -> !transform.any_op
+  %1, %loops:3 = transform.structured.tile %0 [8, 4, 2]
+    : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
+  %2 = get_closest_isolated_parent %1 : (!transform.any_op) -> !transform.any_op
+  transform.structured.vectorize %2 : (!transform.any_op) -> !transform.any_op
   %b = transform.bufferization.one_shot_bufferize
       layout{IdentityLayoutMap} %module_op
       {bufferize_function_boundaries = true, allow_return_allocs = true}
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %f = transform.structured.match ops{["func.func"]} in %b
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   // TODO: group these lower-level controls into various properly named vector
   // lowering TD macros.
   %func = transform.vector.lower_contraction %f
     lowering_strategy = "outerproduct"
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_2 = transform.vector.apply_transfer_permutation_patterns %func
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_3 = transform.vector.lower_multi_reduction %func_2
     lowering_strategy = "innerparallel"
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_4 = transform.vector.split_transfer_full_partial %func_3
     split_transfer_strategy = "linalg-copy"
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_5 = transform.vector.transfer_to_scf %func_4
     max_transfer_rank = 1 full_unroll = true
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_6 = transform.vector.lower_transfer %func_5
     max_transfer_rank = 1
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 
   %func_7 = transform.vector.lower_shape_cast %func_6
-    : (!pdl.operation) -> !pdl.operation
+    : (!transform.any_op) -> !transform.any_op
 
   %func_8 = transform.vector.lower_transpose %func_7
     lowering_strategy = "shuffle_1d"
-      : (!pdl.operation) -> !pdl.operation
+      : (!transform.any_op) -> !transform.any_op
 }