[mlir][nfc] Update vectorization test for scalar broadcast

This PR updates the `@vectorize_scalar_broadcast_column_tensor` test in
"vectorize-tensor-extract.mlir", which exercises:

  * Vectorization of tensor.extract.
  * A scalar read followed by a broadcast.
  * Reading from a constant column tensor.

Currently, the test uses "masked" vectorization, but the file
exclusively tests unmasked vectorization paths. To address this
inconsistency, this PR removes masking, aligning the test with the rest
of the file. Masked vectorization scenarios remain covered in
"vectorize-tensor-extract-masked.mlir". This update switches from:

  * `transform.structured.vectorize`, to
  * `transform.structured.vectorize_children_and_apply_patterns`.

The latter approach applies canonicalization patterns, significantly
simplifying the generated output.

Additional improvements for readability:

  * Renamed the test function for clarity.
  * Updated variable names and removed unused variables.
  * Added empty lines for better formatting.

Author: banach-space

mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir

@@ -807,56 +807,41 @@ module attributes {transform.with_named_sequence} {
 
 // -----
 
-func.func @vectorize_scalar_broadcast_column_tensor(%in: tensor<1x1x4xi32>) -> tensor<1x1x4xi32> {
+func.func @vectorize_scalar_read_with_broadcast_from_column_tensor(%init: tensor<1x1x4xi32>) -> tensor<1x1x4xi32> {
   %c4 = arith.constant 4 : index
   %c0 = arith.constant 0 : index
-  %cst = arith.constant dense<[[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]]> : tensor<15x1xi32>
-
-  %out = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>], iterator_types = ["parallel", "parallel", "parallel"]} outs(%in : tensor<1x1x4xi32>) {
-  ^bb0(%out: i32):
-    %8 = linalg.index 0 : index
-    %idx_0 = linalg.index 0 : index
-    %extracted = tensor.extract %cst[%idx_0, %c0] : tensor<15x1xi32>
-    linalg.yield %extracted : i32
+  %src = arith.constant dense<[[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]]> : tensor<15x1xi32>
+
+  %res = linalg.generic {
+    indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
+    iterator_types = ["parallel", "parallel", "parallel"]}
+    outs(%init : tensor<1x1x4xi32>) {
+
+    ^bb0(%out: i32):
+      %idx = linalg.index 0 : index
+      %extracted = tensor.extract %src[%idx, %c0] : tensor<15x1xi32>
+      linalg.yield %extracted : i32
   } -> tensor<1x1x4xi32>
 
-  return %out:tensor<1x1x4xi32>
+  return %res : tensor<1x1x4xi32>
 }
 
-// CHECK: #[[$MAP:.+]] = affine_map<(d0, d1) -> (0, 0, 0)>
-// CHECK-LABEL:   func.func @vectorize_scalar_broadcast_column_tensor(
-// CHECK-SAME:      %[[VAL_0:.*]]: tensor<1x1x4xi32>) -> tensor<1x1x4xi32> {
-// CHECK:           %[[VAL_1:.*]] = arith.constant 4 : index
-// CHECK:           %[[VAL_2:.*]] = arith.constant 0 : index
-// CHECK:           %[[VAL_3:.*]] = arith.constant dense<{{\[\[}}0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]]> : tensor<15x1xi32>
-// CHECK:           %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK:           %[[VAL_6:.*]] = arith.constant 4 : index
-// CHECK:           %[[VAL_7:.*]] = arith.constant 0 : index
-// CHECK:           %[[VAL_8:.*]] = arith.constant 0 : i32
-// CHECK:           %[[VAL_9:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_7]], %[[VAL_7]], %[[VAL_7]]], %[[VAL_8]] : tensor<1x1x4xi32>, vector<1x1x4xi32>
-// CHECK:           %[[VAL_10:.*]] = vector.step : vector<1xindex>
-// CHECK:           %[[VAL_11:.*]] = vector.broadcast %[[VAL_10]] : vector<1xindex> to vector<4x1x1xindex>
-// CHECK:           %[[VAL_12:.*]] = vector.transpose %[[VAL_11]], [2, 1, 0] : vector<4x1x1xindex> to vector<1x1x4xindex>
-// CHECK:           %[[VAL_13:.*]] = vector.step : vector<1xindex>
-// CHECK:           %[[VAL_14:.*]] = vector.broadcast %[[VAL_13]] : vector<1xindex> to vector<4x1x1xindex>
-// CHECK:           %[[VAL_15:.*]] = vector.transpose %[[VAL_14]], [2, 1, 0] : vector<4x1x1xindex> to vector<1x1x4xindex>
-// CHECK:           %[[VAL_16:.*]] = arith.constant dense<true> : vector<1x1x4xi1>
-// CHECK:           %[[VAL_17:.*]] = arith.constant dense<0> : vector<1x1x4xi32>
-// CHECK:           %[[VAL_18:.*]] = arith.constant 0 : index
-// CHECK:           %[[VAL_19:.*]] = vector.shape_cast %[[VAL_15]] : vector<1x1x4xindex> to vector<4xindex>
-// CHECK:           %[[VAL_20:.*]] = vector.extract %[[VAL_19]][0] : index from vector<4xindex>
-// CHECK:           %[[VAL_21:.*]] = arith.constant 0 : i32
-// CHECK:           %[[VAL_22:.*]] = vector.constant_mask [1] : vector<1xi1>
-// CHECK:           %[[VAL_23:.*]] = vector.mask %[[VAL_22]] { vector.transfer_read %[[VAL_3]]{{\[}}%[[VAL_20]], %[[VAL_2]]], %[[VAL_21]] {in_bounds = [true, true, true], permutation_map = #[[$MAP]]} : tensor<15x1xi32>, vector<1x1x4xi32> } : vector<1xi1> -> vector<1x1x4xi32>
-// CHECK:           %[[VAL_24:.*]] = arith.constant 0 : index
-// CHECK:           %[[VAL_25:.*]] = vector.transfer_write %[[VAL_23]], %[[VAL_0]]{{\[}}%[[VAL_24]], %[[VAL_24]], %[[VAL_24]]] : vector<1x1x4xi32>, tensor<1x1x4xi32>
-// CHECK:           return %[[VAL_25]] : tensor<1x1x4xi32>
+// CHECK-LABEL:   func.func @vectorize_scalar_read_with_broadcast_from_column_tensor(
+// CHECK-SAME:      %[[INIT:.*]]: tensor<1x1x4xi32>) -> tensor<1x1x4xi32> {
+// CHECK:           %[[PAD:.*]] = arith.constant 0 : i32
+// CHECK:           %[[C0:.*]] = arith.constant 0 : index
+// CHECK:           %[[SRC:.*]] = arith.constant dense<{{\[\[}}0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14]]> : tensor<15x1xi32>
+// CHECK:           %[[IDX_VEC:.*]] = arith.constant dense<0> : vector<1xindex>
+// CHECK:           %[[IDX_ELT:.*]] = vector.extract %[[IDX_VEC]][0] : index from vector<1xindex>
+// CHECK:           %[[READ:.*]] = vector.transfer_read %[[SRC]]{{\[}}%[[IDX_ELT]], %[[C0]]], %[[PAD]] : tensor<15x1xi32>, vector<i32>
+// CHECK:           %[[READ_BCAST:.*]] = vector.broadcast %[[READ]] : vector<i32> to vector<1x1x4xi32>
+// CHECK:           %[[RES:.*]] = vector.transfer_write %[[READ_BCAST]], %[[INIT]][%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<1x1x4xi32>, tensor<1x1x4xi32>
 
 module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
-    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
-    transform.structured.vectorize %0  vector_sizes [1, 1, 4]{ vectorize_nd_extract } : !transform.any_op
+     %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+     %1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+     %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
     transform.yield
   }
 }