[Mlir-commits] [mlir] [NFC] Make AggregateOpInterface part of mlir:: instead of linalg:: (PR #70089)

[Mehdi Amini]

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@@ -0,0 +1,57 @@
+//===- AggregatedOpInterface.td ----------------------*- tablegen -*-===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef MLIR_AGGREGATEDOPINTERFACE
+#define MLIR_AGGREGATEDOPINTERFACE
+
+include "mlir/IR/OpBase.td"
+
+def AggregatedOpInterface : OpInterface<"AggregatedOpInterface"> {
+  let description = [{
+    This Interface is particularly useful in cases where we have an operation
+    that can be lowered into a sequence of simpler operations, thus essentially
+    decomposing an operation into a set of one or many simpler operations.
+    The operation being decomposed need to implement this Interface by implementing
+    the method `decomposeOperation` and return the set of values which would replace
+    the uses of the operation being decomposed.
+    Eg:
+        Assume there is an operation `CustomOp_Mul_Add` that takes in an input tensor
+        and a constant. It basically performs element-wise multiplication of the input
+        tensor with the given constant, and then performs element-wise addition of the
+        intermediate resulting tensor with the given constant.
+        `CustomOp_Mul_Add` can thus essentially be decomposed by implementing this
+        Interface.
+        `linalg::SoftmaxOp` is one such operation which makes use of this Interface
+        for implementing its decomposition.
+  }];
----------------
joker-eph wrote:

I have a hard time seeing how this is something that composes across dialects or can just be reused generically. That seems quite "specific" to a specific pass at one given level of abstraction (and this is what makes it pretty contextual and not generic as I see it right now).

https://github.com/llvm/llvm-project/pull/70089