[Mlir-commits] [mlir] [mlir][linalg] Extend elementwise (PR #124661)

[Rolf Morel]

================
@@ -0,0 +1,88 @@
+// RUN: mlir-opt %s -split-input-file | FileCheck %s
+//
+// Note - the functions are named @{unary|binary}_{identity|transpose|broadcast|transpose_a|...}_{exp|mul|div|..}
+
+// CHECK: @unary_identity_exp(%[[A:.+]]: tensor<8x16x32xf32>, %[[B:.+]]: tensor<8x16x32xf32>)
+// CHECK: %{{.*}} = linalg.elementwise kind=#linalg.elementwise_fn<exp>
+// CHECK-SAME         ins(%[[A:.+]] : tensor<8x16x32xf32>) outs(%[[B:.+]] : tensor<8x16x32xf32>)
+//
+func.func @unary_identity_exp(%A : tensor<8x16x32xf32>, %B: tensor<8x16x32xf32>) ->  tensor<8x16x32xf32> {
+  %r = linalg.elementwise
+         kind=#linalg.elementwise_fn<exp>
+         ins(%A : tensor<8x16x32xf32>)
+         outs(%B: tensor<8x16x32xf32>) -> tensor<8x16x32xf32>
+  return %r : tensor<8x16x32xf32>
+}
+
+// -----
+
+// CHECK-DAG: #[[IDENTITY:.+]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[PROJECTION:.+]] = affine_map<(d0, d1, d2) -> (d2, d1)>
+//
+// CHECK: @unary_projection_tanh(%[[A:.+]]: tensor<?x16xf32>,
+// CHECK-SAME:                            %[[B:.+]]: tensor<8x16x?xf32>) ->  tensor<8x16x?xf32> {
+// CHECK: {{.*}} = linalg.elementwise kind=#linalg.elementwise_fn<tanh>
+// CHECK-SAME:       indexing_maps = [#[[PROJECTION]], #[[IDENTITY]]]
+// CHECK-SAME:       ins(%[[A]] : tensor<?x16xf32>) outs(%[[B]] : tensor<8x16x?xf32>) -> tensor<8x16x?xf32>
+//
+func.func @unary_projection_tanh(%A: tensor<?x16xf32>,
+                                          %B: tensor<8x16x?xf32>) ->  tensor<8x16x?xf32> {
+  %r = linalg.elementwise
+         kind=#linalg.elementwise_fn<tanh>
+         indexing_maps = [affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1, d2)>]
+         ins(%A : tensor<?x16xf32>)
+         outs(%B: tensor<8x16x?xf32>) -> tensor<8x16x?xf32>
+  return %r : tensor<8x16x?xf32>
+}
+
+// -----
+
+// CHECK: @binary_identity_div(%[[A:.+]]: tensor<16x8xf32>, %[[B:.+]]: tensor<16x8xf32>,
+// CHECK-SAME:        %[[C:.+]]: tensor<16x8xf32>) ->  tensor<16x8xf32> {
+// CHECK: {{.*}} = linalg.elementwise
+// CHECK-SAME:       kind=#linalg.elementwise_fn<div>
+// CHECK-SAME:       ins(%[[A]], %[[B]] : tensor<16x8xf32>, tensor<16x8xf32>)
+// CHECK-SAME:       outs(%[[C]] : tensor<16x8xf32>) -> tensor<16x8xf32>
+//
+func.func @binary_identity_div(%A: tensor<16x8xf32>, %B: tensor<16x8xf32>,
+                      %C: tensor<16x8xf32>) ->  tensor<16x8xf32> {
+  %r = linalg.elementwise
+         kind=#linalg.elementwise_fn<div>
+         ins(%A, %B: tensor<16x8xf32>, tensor<16x8xf32>)
+         outs(%C: tensor<16x8xf32>) -> tensor<16x8xf32>
+  return %r : tensor<16x8xf32>
+}
+
+// -----
+
+// CHECK: @binary_identity_mul_5Di(%[[A]]: tensor<1x2x3x4x5xi32>,
+// CHECK-SAME:                     %[[B:.+]]: tensor<1x2x3x4x5xi32>,
+// CHECK-SAME:                     %[[C:.+]]: tensor<1x2x3x4x5xi32>) -> tensor<1x2x3x4x5xi32> {
+// CHECK: {{.*}} = linalg.elementwise
+// CHECK-SAME:       kind=#linalg.elementwise_fn<mul>
+// CHECK-SAME:       ins(%[[A]], %[[B]] : tensor<1x2x3x4x5xi32>, tensor<1x2x3x4x5xi32>)
+// CHECK-SAME:       outs(%[[C]] : tensor<1x2x3x4x5xi32>) -> tensor<1x2x3x4x5xi32>
+//
+func.func @binary_identity_mul_5Di(%A: tensor<1x2x3x4x5xi32>, %B: tensor<1x2x3x4x5xi32>,
+                                   %C: tensor<1x2x3x4x5xi32>) ->  tensor<1x2x3x4x5xi32> {
+  %r = linalg.elementwise
+         kind=#linalg.elementwise_fn<mul>
+         ins(%A, %B: tensor<1x2x3x4x5xi32>, tensor<1x2x3x4x5xi32>)
+         outs(%C: tensor<1x2x3x4x5xi32>) -> tensor<1x2x3x4x5xi32>
+  return %r : tensor<1x2x3x4x5xi32>
+}
+
+// -----
+
+// CHECK: @redundant_maps
+// CHECK-NOT: indexing_maps
+//
+#map = affine_map<(d0, d1, d2, d3, d4) -> (d0, d1, d2, d3, d4)>
+func.func @redundant_maps(%A: tensor<1x2x3x4x5xi32>, %B: tensor<1x2x3x4x5xi32>,
+                          %C: tensor<1x2x3x4x5xi32>) ->  tensor<1x2x3x4x5xi32> {
+  %r = linalg.elementwise   kind=#linalg.elementwise_fn<mul>
----------------
rolfmorel wrote:

nit: kind is on own line everywhere else

https://github.com/llvm/llvm-project/pull/124661