Use linalg.elementwise and fix fuse test to match previous behaviour

Author: rengolin

mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir

@@ -26,8 +26,8 @@ module {
       // CHECK: %[[T1:.*]] = linalg.fill {{.*}} outs(%[[T0]]
       %6 = tensor.extract_slice %0[%3] [%4] [1] : tensor<?xf32> to tensor<?xf32>
 
-      // CHECK: %[[T2:.*]] = linalg.exp ins(%[[T1]]
-      %7 = linalg.exp ins(%6 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
+      // CHECK: %[[T2:.*]] = linalg.elementwise kind=#linalg.elementwise_kind<exp> ins(%[[T1]]
+      %7 = linalg.elementwise kind=#linalg.elementwise_kind<exp> ins(%6 : tensor<?xf32>) outs(%5 : tensor<?xf32>) -> tensor<?xf32>
       scf.forall.in_parallel {
         tensor.parallel_insert_slice %7 into %o[%3] [%4] [1] : tensor<?xf32> into tensor<?xf32>
       }
@@ -261,7 +261,7 @@ module {
     %c2 = arith.constant 2 : index
     %c1 = arith.constant 1 : index
     %c0 = arith.constant 0 : index
-    %0 = linalg.exp {fun = #linalg.unary_fn<abs>} ins(%arg0 : tensor<?x?x?xf32>) outs(%arg1 : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
+    %0 = linalg.elementwise kind=#linalg.elementwise_kind<abs> ins(%arg0 : tensor<?x?x?xf32>) outs(%arg1 : tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
     %dim = tensor.dim %arg1, %c0 : tensor<?x?x?xf32>
     %dim_0 = tensor.dim %arg1, %c1 : tensor<?x?x?xf32>
     %dim_1 = tensor.dim %arg1, %c2 : tensor<?x?x?xf32>
@@ -272,12 +272,12 @@ module {
       %2 = scf.for %arg4 = %c0 to %dim_0 step %c1 iter_args(%arg5 = %arg3) -> (tensor<?x?x?xf32>) {
         %3 = scf.for %arg6 = %c0 to %dim_1 step %c1 iter_args(%arg7 = %arg5) -> (tensor<?x?x?xf32>) {
           // CHECK:  %[[EX1:.*]] = tensor.extract_slice %[[BBARG2]]{{.*}}: tensor<?x?x?xf32> to tensor<1x1x1xf32>
-          // CHECK:  linalg.exp {fun = #linalg.unary_fn<abs>} ins({{.*}} : tensor<1x1x1xf32>) outs(%[[EX1]] : tensor<1x1x1xf32>) -> tensor<1x1x1xf32>
+          // CHECK:  linalg.elementwise kind=#linalg.elementwise_kind<abs> ins({{.*}} : tensor<1x1x1xf32>) outs(%[[EX1]] : tensor<1x1x1xf32>) -> tensor<1x1x1xf32>
           // CHECK:  %[[EX2:.*]] = tensor.extract_slice %[[BBARG2]]{{.*}} : tensor<?x?x?xf32> to tensor<1x1x1xf32>
-          // CHECK:  linalg.exp {fun = #linalg.unary_fn<exp>} ins({{.*}} : tensor<1x1x1xf32>) outs(%[[EX2]] : tensor<1x1x1xf32>) -> tensor<1x1x1xf32>
+          // CHECK:  linalg.elementwise kind=#linalg.elementwise_kind<exp> ins({{.*}} : tensor<1x1x1xf32>) outs(%[[EX2]] : tensor<1x1x1xf32>) -> tensor<1x1x1xf32>
           %extracted_slice = tensor.extract_slice %0[%arg2, %arg4, %arg6] [1, 1, 1] [1, 1, 1] : tensor<?x?x?xf32> to tensor<1x1x1xf32>
           %extracted_slice_2 = tensor.extract_slice %arg7[%arg2, %arg4, %arg6] [1, 1, 1] [1, 1, 1] : tensor<?x?x?xf32> to tensor<1x1x1xf32>
-          %4 = linalg.exp {fun = #linalg.unary_fn<exp>} ins(%extracted_slice : tensor<1x1x1xf32>) outs(%extracted_slice_2 : tensor<1x1x1xf32>) -> tensor<1x1x1xf32>
+          %4 = linalg.elementwise kind=#linalg.elementwise_kind<exp> ins(%extracted_slice : tensor<1x1x1xf32>) outs(%extracted_slice_2 : tensor<1x1x1xf32>) -> tensor<1x1x1xf32>
           %inserted_slice = tensor.insert_slice %4 into %arg7[%arg2, %arg4, %arg6] [1, 1, 1] [1, 1, 1] : tensor<1x1x1xf32> into tensor<?x?x?xf32>
           scf.yield %inserted_slice : tensor<?x?x?xf32>
         }
@@ -290,7 +290,7 @@ module {
 
   module attributes {transform.with_named_sequence} {
     transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
-      %0 = transform.structured.match ops{["linalg.exp"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+      %0 = transform.structured.match ops{["linalg.elementwise"]} in %arg0 : (!transform.any_op) -> !transform.any_op
       %1 = transform.structured.match ops{["scf.for"]} in %arg0 : (!transform.any_op) -> !transform.any_op
       %2:2 = transform.split_handle %0 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
       %3:3 = transform.split_handle %1 : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)