[MLIR] Drop assumption of a surrounding builtin.func in promoteIfSingleIteration (#116323)

Drop assumption of a surrounding builtin.func in
promoteIfSingleIteration.
Fixes #116042

Author: bondhugula

mlir/lib/Dialect/Affine/Utils/LoopUtils.cpp

@@ -129,8 +129,13 @@ LogicalResult mlir::affine::promoteIfSingleIteration(AffineForOp forOp) {
   auto *parentBlock = forOp->getBlock();
   if (!iv.use_empty()) {
     if (forOp.hasConstantLowerBound()) {
-      OpBuilder topBuilder(forOp->getParentOfType<func::FuncOp>().getBody());
-      auto constOp = topBuilder.create<arith::ConstantIndexOp>(
+      auto func = forOp->getParentOfType<FunctionOpInterface>();
+      OpBuilder builder(forOp->getContext());
+      if (func)
+        builder.setInsertionPointToStart(&func.getFunctionBody().front());
+      else
+        builder.setInsertionPoint(forOp);
+      auto constOp = builder.create<arith::ConstantIndexOp>(
           forOp.getLoc(), forOp.getConstantLowerBound());
       iv.replaceAllUsesWith(constOp);
     } else {

mlir/test/Dialect/Affine/loop-fusion-4.mlir

@@ -1,5 +1,6 @@
 // RUN: mlir-opt -allow-unregistered-dialect %s -pass-pipeline='builtin.module(func.func(affine-loop-fusion{mode=producer}))' -split-input-file | FileCheck %s --check-prefix=PRODUCER-CONSUMER
 // RUN: mlir-opt -allow-unregistered-dialect %s -pass-pipeline='builtin.module(func.func(affine-loop-fusion{fusion-maximal mode=sibling}))' -split-input-file | FileCheck %s --check-prefix=SIBLING-MAXIMAL
+// RUN: mlir-opt -allow-unregistered-dialect %s -pass-pipeline='builtin.module(spirv.func(affine-loop-fusion{mode=producer}))' -split-input-file | FileCheck %s --check-prefix=SPIRV
 
 // Part I of fusion tests in  mlir/test/Transforms/loop-fusion.mlir.
 // Part II of fusion tests in mlir/test/Transforms/loop-fusion-2.mlir
@@ -226,3 +227,61 @@ func.func @fuse_higher_dim_nest_into_lower_dim_nest() {
   // PRODUCER-CONSUMER:      return
   return
 }
+
+// -----
+
+// Basic test to ensure fusion works inside other func ops like spirv.func.
+
+#map = affine_map<(d0, d1) -> (d0 + d1)>
+module {
+  // SPIRV-LABEL: func @test_avgpool2d_pad_right
+  spirv.func @test_avgpool2d_pad_right(%arg0: !spirv.array<8192 x f32>) -> !spirv.array<8192 x f32> "None" {
+    %cst_f32 = spirv.Constant 0.000000e+00 : f32
+    %0 = builtin.unrealized_conversion_cast %arg0 : !spirv.array<8192 x f32> to tensor<1x32x32x8xf32>
+    %padded = tensor.pad %0 low[0, 4, 4, 0] high[0, 4, 8193, 0] {
+    ^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
+      tensor.yield %cst_f32 : f32
+    } : tensor<1x32x32x8xf32> to tensor<1x40x8229x8xf32>
+    %1 = bufferization.to_memref %padded : memref<1x40x8229x8xf32>
+    %alloc_0 = memref.alloc() {alignment = 64 : i64} : memref<1x32x32x8xf32>
+    affine.for %arg1 = 0 to 1 {
+      affine.for %arg2 = 0 to 32 {
+        affine.for %arg3 = 0 to 32 {
+          affine.for %arg4 = 0 to 8 {
+            affine.for %arg5 = 0 to 1 {
+              affine.for %arg6 = 0 to 1 {
+                %4 = affine.apply #map(%arg2, %arg5)
+                %5 = affine.apply #map(%arg3, %arg6)
+                %6 = affine.load %1[%arg1, %4, %5, %arg4] : memref<1x40x8229x8xf32>
+                %7 = affine.load %alloc_0[%arg1, %arg2, %arg3, %arg4] : memref<1x32x32x8xf32>
+                %8 = arith.addf %7, %6 : f32
+                affine.store %8, %alloc_0[%arg1, %arg2, %arg3, %arg4] : memref<1x32x32x8xf32>
+              }
+            }
+          }
+        }
+      }
+    }
+    %alloc_1 = memref.alloc() {alignment = 64 : i64} : memref<1x32x32x8xf32>
+    affine.for %arg1 = 0 to 1 {
+      affine.for %arg2 = 0 to 32 {
+        affine.for %arg3 = 0 to 32 {
+          affine.for %arg4 = 0 to 8 {
+            %4 = affine.load %alloc_0[%arg1, %arg2, %arg3, %arg4] : memref<1x32x32x8xf32>
+          }
+        }
+      }
+    }
+    // Test fusion.
+    // SPIRV:      affine.for %{{.*}} = 0 to 1 {
+    // SPIRV-NEXT:   affine.for %{{.*}} = 0 to 32 {
+    // SPIRV-NEXT:     affine.for %{{.*}} = 0 to 32 {
+    // SPIRV-NEXT:       affine.for %{{.*}} = 0 to 8 {
+    // SPIRV-NOT:       affine.for %{{.*}}
+
+    // SPIRV:       ReturnValue
+    %2 = bufferization.to_tensor %alloc_1 : memref<1x32x32x8xf32>
+    %3 = builtin.unrealized_conversion_cast %2 : tensor<1x32x32x8xf32> to !spirv.array<8192 x f32>
+    spirv.ReturnValue %3 : !spirv.array<8192 x f32>
+  }
+}