[mlir][nfc] Update vectorize-tensor-extract.mlir (1/N)

Tests in "vectorize-tensor-extract.mlir" are inconsistent and would
benefit from refactoring to:

* Clearly categorize tests into "contiguous load," "gather load," and
  "scalar load + broadcast" cases, reflecting the structure of
  tensor.extract vectorization.
* Unify variable naming (both MLIR and FileCheck).
* Ensure all tests exercise unmasked vectorization (masked vectorization
  is covered in "vectorize-tensor-extract-masked.mlir").
* Improve and standardize formatting.

These changes will make it easier to identify the test cases being
exercised and simplify future maintenance or refactoring.

This is patch 1/N in the series. Below is a summary of the changes in
this patch.

----------------------------------------------------------------------

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
   }
 }