[Mlir-commits] [mlir] [mlir][nfc] Update vectorize-tensor-extract.mlir (2/N) (PR #119080)

[Han-Chung Wang]

================
@@ -92,51 +76,35 @@ func.func @extract_scalar_from_0d_into_1d(%src: tensor<f32>, %init: tensor<1xf32
 // CHECK:           %[[READ_BCAST:.*]] = vector.broadcast %[[READ]] : vector<f32> to vector<1xf32>
 // CHECK:           vector.transfer_write %[[READ_BCAST]], %[[INIT]][%[[C0]]] {in_bounds = [true]} : vector<1xf32>, tensor<1xf32>
 
-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
-    %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
-  }
-}
-
 // -----
 
 #map = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
-func.func @vectorize_nd_tensor_extract_scalar_broadcast(%arg0: tensor<3x3xf32>, %arg2: tensor<1x1x3xf32>) -> tensor<1x1x3xf32> {
+func.func @vectorize_nd_tensor_extract_scalar_broadcast(%src: tensor<3x3xf32>, %init: tensor<1x1x3xf32>) -> tensor<1x1x3xf32> {
   %c0 = arith.constant 1 : index
   %c1 = arith.constant 2 : index
-  %2 = linalg.generic {
+
+  %res = linalg.generic {
     indexing_maps = [#map],
     iterator_types = ["parallel", "parallel", "parallel"]
-  } outs(%arg2 : tensor<1x1x3xf32>) {
+  } outs(%init : tensor<1x1x3xf32>) {
   ^bb0(%arg4: f32):
-    %7 = tensor.extract %arg0[%c0, %c1] : tensor<3x3xf32>
-    linalg.yield %7 : f32
+    %1 = tensor.extract %src[%c0, %c1] : tensor<3x3xf32>
+    linalg.yield %1 : f32
   } -> tensor<1x1x3xf32>
-  return %2 : tensor<1x1x3xf32>
+
+  return %res : tensor<1x1x3xf32>
 }
 
-// CHECK: #[[$MAP:.+]] = affine_map<(d0, d1) -> (0, 0, 0)>
 // CHECK-LABEL:   func.func @vectorize_nd_tensor_extract_scalar_broadcast(
-// CHECK-SAME:      %[[ARG_0:.*]]: tensor<3x3xf32>,
-// CHECK-SAME:      %[[ARG_1:.*]]: tensor<1x1x3xf32>) -> tensor<1x1x3xf32> {
-// CHECK-DAG:       %[[C1:.*]] = arith.constant 1 : index
-// CHECK-DAG:       %[[C2:.*]] = arith.constant 2 : index
-// CHECK-DAG:       %[[C0:.*]] = arith.constant 0 : index
-// CHECK:           %[[MASK:.*]] = vector.constant_mask [1] : vector<1xi1>
-// CHECK:           %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[ARG_0]][%[[C1]], %[[C2]]], {{.*}} {in_bounds = [true, true, true], permutation_map = #[[$MAP]]} : tensor<3x3xf32>, vector<1x1x3xf32> } : vector<1xi1> -> vector<1x1x3xf32>
-// CHECK:           %[[C0_2:.*]] = arith.constant 0 : index
-// CHECK:           vector.transfer_write %[[READ]], %[[ARG_1]]{{\[}}%[[C0_2]], %[[C0_2]], %[[C0_2]]] : vector<1x1x3xf32>, tensor<1x1x3xf32>
-
-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 { vectorize_nd_extract }  : !transform.any_op
-    transform.yield
-   }
-}
+// CHECK-SAME:      %[[SRC:.*]]: tensor<3x3xf32>,
+// CHECK-SAME:      %[[INIT:.*]]: tensor<1x1x3xf32>) -> tensor<1x1x3xf32> {
+// CHECK:           %[[C0:.*]] = arith.constant 0 : index
+// CHECK:           %[[C1:.*]] = arith.constant 1 : index
+// CHECK:           %[[C2:.*]] = arith.constant 2 : index
+// CHECK:           %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
----------------
hanhanW wrote:

Why not using `CHECK-DAG` for constants? I remember that there was a commit could break the order. Using `CHECK-DAG` makes life easier if it happens again.

https://github.com/llvm/llvm-project/pull/119080