[mlir][linalg] Mark linalg.yield as ReturnLike

[herhut]

Summary

This change is required so that bufferization can properly identify the linalg.yield as a terminator with an associated parent op.

[bkramer]

looks good

[herhut]

Clean up test invocation

mlir/include/mlir/Dialect/Linalg/IR/LinalgOps.td

 //===- LinalgOps.td - Linalg dialect ops -------------------*- 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
 //
 //===----------------------------------------------------------------------===//
 //
 // This is the operation definition file for linear algebra operations.
 //
 //===----------------------------------------------------------------------===//
 
 #ifndef LINALG_OPS
 #define LINALG_OPS
 
 include "mlir/Dialect/Linalg/IR/LinalgBase.td"
+include "mlir/Interfaces/ControlFlowInterfaces.td"
 include "mlir/Interfaces/SideEffectInterfaces.td"
 include "mlir/Interfaces/ViewLikeInterface.td"
 
 // Base class for Linalg dialect ops that do not correspond to library calls.
 class Linalg_Op<string mnemonic, list<OpTrait> traits = []> :
     Op<Linalg_Dialect, mnemonic, traits> {
   // For every linalg op, there needs to be a:
   //   * void print(OpAsmPrinter &p, ${C++ class of Op} op)
SmallVector<Value, 8> getRanges() {
           res.push_back(operand);
       return res;
     }
   }];
 
   let hasFolder = 1;
 }
 
-def Linalg_YieldOp : Linalg_Op<"yield", [NoSideEffect, Terminator]>,
+def Linalg_YieldOp : Linalg_Op<"yield", [NoSideEffect, ReturnLike, Terminator]>,
     Arguments<(ins Variadic<AnyType>:$values)> {
   let summary = "Linalg yield operation";
   let description = [{
     `linalg.yield` is a special terminator operation for blocks inside regions
     in `linalg` generic ops. It returns values to the immediately enclosing
     `linalg` generic op.
 
     Example:
 
     ```mlir
     linalg.yield %f0, %f1 : f32, f32
     ```
   }];
 }
 
 #endif // LINALG_OPS

mlir/integration_test/Dialect/Linalg/CPU/test-tensor-e2e.mlir

-// RUN: mlir-opt %s -tensor-constant-bufferize -std-bufferize -linalg-bufferize -func-bufferize -convert-linalg-to-loops -convert-linalg-to-llvm -convert-std-to-llvm | \
+// RUN: mlir-opt %s -tensor-constant-bufferize -std-bufferize -linalg-bufferize \
+// RUN: -func-bufferize -finalizing-bufferize -convert-linalg-to-loops \
+// RUN: -convert-linalg-to-llvm -convert-std-to-llvm | \
 // RUN: mlir-cpu-runner -e main -entry-point-result=void \
 // RUN:   -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
 // RUN: | FileCheck %s
 
 func @foo() -> tensor<4xf32> {
   %0 = constant dense<[1.0, 2.0, 3.0, 4.0]> : tensor<4xf32>
   return %0 : tensor<4xf32>
 }

mlir/integration_test/Dialect/Linalg/CPU/test-tensor-matmul.mlir

-// RUN: mlir-opt %s -linalg-bufferize -std-bufferize -tensor-constant-bufferize -func-bufferize \
-// RUN: -convert-linalg-to-loops -convert-linalg-to-llvm -convert-std-to-llvm | \
+// RUN: mlir-opt %s -linalg-bufferize -std-bufferize -tensor-constant-bufferize \
+// RUN: -func-bufferize -finalizing-bufferize  -convert-linalg-to-loops \
+// RUN: -convert-linalg-to-llvm -convert-std-to-llvm | \
 // RUN: mlir-cpu-runner -e main -entry-point-result=void \
 // RUN:   -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
 // RUN: | FileCheck %s
 
 // RUN: mlir-opt %s  -linalg-tile="linalg-tile-sizes=1,2,3" -linalg-bufferize \
-// RUN: -scf-bufferize -std-bufferize -tensor-constant-bufferize -func-bufferize -convert-linalg-to-loops \
-// RUN:  -convert-scf-to-std -convert-linalg-to-llvm | \
+// RUN: -scf-bufferize -std-bufferize -tensor-constant-bufferize -func-bufferize \
+// RUN: -finalizing-bufferize -convert-linalg-to-loops -convert-scf-to-std \
+// RUN: -convert-linalg-to-llvm | \
 // RUN: mlir-cpu-runner -e main -entry-point-result=void \
 // RUN:   -shared-libs=%mlir_integration_test_dir/libmlir_runner_utils%shlibext \
 // RUN: | FileCheck %s
 
 func @main() {
   %A = constant dense<[[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]> : tensor<2x3xf32>
   %B = constant dense<[[1.0, 2.0, 3.0, 4.0],
                        [5.0, 6.0, 7.0, 8.0],