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

[Javed Absar]

================
@@ -551,6 +551,122 @@ def BroadcastOp : LinalgStructuredBase_Op<"broadcast", [
   let hasCanonicalizer = 1;
 }
 
+//===----------------------------------------------------------------------===//
+// Op definition for ElementwiseOp
+//===----------------------------------------------------------------------===//
+def ElementwiseOp : LinalgStructuredBase_Op<"elementwise", [
+                   AttrSizedOperandSegments]> {
+  let summary = [{ Performs element-wise operation }];
+  let description = [{
+    Linalg op form which performs element-wise computation.
+
+    The attribute `kind` describes the operation (e.g. add, exp). The operation
+    kind can be any elementwise nary (e.g. unary, binary) operation.
+
+    Affine-maps for operands and result are reuired to be provided by the user
+    when transpose and/or broadcast is needed on any operand. When a map is not
+    provided, default identity maps are inferred for each operand. The number
+    of dims in each of the identity maps is equal to the rank of the output type.
+    In the case of default indexing map, all input and output shapes must match.
+    User-defined Affine-map for operands and result must only be projected
+    permutations with no zero constants.
+
+    For elementwise, iterator-types are always 'all parallel’.
+    Iterator-types are needed for constructing the underlying structured op.
+    The number of dims of the iterator-types are inferred from the rank of
+    the result type.
+
+    Example:
+
+    Defining a unary linalg.elemwise with default indexing-map:
+      ```mlir
+      %exp = linalg.elemwise
+             kind=#linalg.elemwise_fn<exp>
+             ins(%x : tensor<4x16x8xf32>)
+             outs(%y: tensor<4x16x8xf32>) -> tensor<4x16x8xf32>
+      ```
+
+    Defining a binary linalg.elemwise with user-defined indexing-map:
+    ```mlir
+    %add = linalg.elemwise
+            kind=#linalg.elemwise_fn<add>
+            indexing_maps = [#transpose, #broadcast, #identity]
+            ins(%exp, %arg1 : tensor<4x16x8xf32>, tensor<4x16xf32>)
+            outs(%arg2: tensor<4x8x16xf32>) -> tensor<4x8x16xf32>
+    ```
+  }];
+
+  let arguments = (ins
+      Variadic<AnyType>:$inputs,
----------------
javedabsar1 wrote:

thanks. changed to AnyShaped.

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