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[mlir][Affine] Fix crash in affine-loop-fusion pass by guarding against an empty op list (#144841)

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Related: #139231

This patch fixes a crash in the affine-loop-fusion pass when
`getInnermostCommonLoop` is called with an empty list of operations.

The function expects at least one op to analyze, and passing an empty
array of ops causes an assertion failure. This change ensures the pass
checks for an empty op array before calling `getInnermostCommonLoop`.

@bondhugula @matthias-springer
This commit is contained in:
Ayokunle Amodu
2025-07-02 08:31:49 -06:00
committed by GitHub
parent 5491576a16
commit 38ad6b1983
2 changed files with 79 additions and 0 deletions
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2
mlir/lib/Dialect/Affine/Transforms/LoopFusion.cpp
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@@ -1002,6 +1002,8 @@ public:
if (producerConsumerMemrefs.count( if (producerConsumerMemrefs.count(
cast<AffineWriteOpInterface>(op).getMemRef())) cast<AffineWriteOpInterface>(op).getMemRef()))
dstMemrefOps.push_back(op); dstMemrefOps.push_back(op);
if (dstMemrefOps.empty())
continue;
unsigned dstLoopDepthTest = unsigned dstLoopDepthTest =
getInnermostCommonLoopDepth(dstMemrefOps) - numSurroundingLoops; getInnermostCommonLoopDepth(dstMemrefOps) - numSurroundingLoops;

77
mlir/test/Dialect/Affine/loop-fusion-4.mlir
View File

@@ -666,3 +666,80 @@ func.func @unrolled(%arg0: memref<2x4xf32>, %arg1: memref<1x2x4xf32>) {
// PRODUCER-CONSUMER-MAXIMAL: affine.load %{{.*}}[0, %{{.*}}, %{{.*}}] // PRODUCER-CONSUMER-MAXIMAL: affine.load %{{.*}}[0, %{{.*}}, %{{.*}}]
return return
} }
// -----
// Exercises fix for crash reported at https://github.com/llvm/llvm-project/issues/139231
#map = affine_map<(d0, d1) -> (d0 + d1)>
#map1 = affine_map<(d0, d1) -> (d0 * 2 + d1 * 2)>
module {
func.func @zero_candidates() {
%cst = arith.constant 2.221140e+03 : f32
%cst_0 = arith.constant 2.606200e+03 : f32
%cst_1 = arith.constant 3.224000e+03 : f32
%cst_2 = arith.constant 0.000000e+00 : f32
%alloc = memref.alloc() {alignment = 64 : i64} : memref<3x7x5x6xf32>
affine.for %arg0 = 0 to 3 {
affine.for %arg1 = 0 to 7 {
affine.for %arg2 = 0 to 5 {
affine.for %arg3 = 0 to 6 {
affine.store %cst_1, %alloc[%arg0, %arg1, %arg2, %arg3] : memref<3x7x5x6xf32>
}
}
}
}
%alloc_3 = memref.alloc() {alignment = 64 : i64} : memref<3x10x7x6xf32>
%subview = memref.subview %alloc_3[0, 2, 1, 0] [3, 7, 5, 6] [1, 1, 1, 1] : memref<3x10x7x6xf32> to memref<3x7x5x6xf32, strided<[420, 42, 6, 1], offset: 90>>
memref.copy %alloc, %subview : memref<3x7x5x6xf32> to memref<3x7x5x6xf32, strided<[420, 42, 6, 1], offset: 90>>
%alloc_4 = memref.alloc() {alignment = 64 : i64} : memref<3x10x3x6x1xf32>
affine.for %arg0 = 0 to 3 {
affine.for %arg1 = 0 to 10 {
affine.for %arg2 = 0 to 3 {
affine.for %arg3 = 0 to 6 {
affine.for %arg4 = 0 to 1 {
affine.store %cst_2, %alloc_4[%arg0, %arg1, %arg2, %arg3, %arg4] : memref<3x10x3x6x1xf32>
}
}
}
}
}
affine.for %arg0 = 0 to 3 {
affine.for %arg1 = 0 to 10 {
affine.for %arg2 = 0 to 3 {
affine.for %arg3 = 0 to 6 {
affine.for %arg4 = 0 to 1 {
affine.for %arg5 = 0 to 1 {
affine.for %arg6 = 0 to 2 {
%0 = affine.apply #map(%arg1, %arg5)
%1 = affine.apply #map1(%arg2, %arg6)
%2 = affine.load %alloc_3[%arg0, %0, %1, %arg3] : memref<3x10x7x6xf32>
%3 = affine.load %alloc_4[%arg0, %arg1, %arg2, %arg3, %arg4] : memref<3x10x3x6x1xf32>
%4 = arith.mulf %2, %cst_0 : f32
%5 = arith.addf %3, %4 : f32
affine.store %5, %alloc_4[%arg0, %arg1, %arg2, %arg3, %arg4] : memref<3x10x3x6x1xf32>
}
}
}
}
}
}
}
%alloc_5 = memref.alloc() {alignment = 64 : i64} : memref<3x10x3x6xf32>
%expand_shape = memref.expand_shape %alloc_5 [[0], [1], [2], [3, 4]] output_shape [3, 10, 3, 6, 1] : memref<3x10x3x6xf32> into memref<3x10x3x6x1xf32>
affine.for %arg0 = 0 to 3 {
affine.for %arg1 = 0 to 10 {
affine.for %arg2 = 0 to 3 {
affine.for %arg3 = 0 to 6 {
affine.for %arg4 = 0 to 1 {
%0 = affine.load %alloc_4[%arg0, %arg1, %arg2, %arg3, %arg4] : memref<3x10x3x6x1xf32>
%1 = arith.addf %0, %cst : f32
affine.store %1, %expand_shape[%arg0, %arg1, %arg2, %arg3, %arg4] : memref<3x10x3x6x1xf32>
}
}
}
}
}
return
}
}