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[mlir][linalg] fix segmentation fault in isContractionBody function #108703

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BRUCE11111 merged 2 commits into from
Sep 27, 2024
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[mlir][linalg] fix segmentation fault in isContractionBody function #108703

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18e8c0f
[mlir][linalg] fix segmentation fault in isContractionBody function
Sep 14, 2024
0b61073
[mlir][linalg] add a simple test
Sep 18, 2024
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2 changes: 1 addition & 1 deletion mlir/lib/Dialect/Linalg/IR/LinalgInterfaces.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -222,7 +222,7 @@ bool mlir::linalg::detail::isContractionBody(
Value contributed = getSourceSkipUnary(
isa<BlockArgument>(reductionLHS) ? reductionRHS : reductionLHS);
Operation *elementwiseOp = contributed.getDefiningOp();
if (elementwiseOp->getNumResults() != 1 ||
if (!elementwiseOp || elementwiseOp->getNumResults() != 1 ||
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@joker-eph joker-eph Sep 15, 2024

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Can you please provide a minimal test for this?

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@BRUCE11111 BRUCE11111 Sep 18, 2024 •
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Hi~ Added to existing test for testing this method. This test can reproduce the problem of the current method.

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@BRUCE11111 BRUCE11111 Sep 20, 2024

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Hello~ Could you please help me to approve this PR? Thanks~

elementwiseOp->getNumOperands() != 2) {
errs << "expected elementwise op to be binary";
return false;
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15 changes: 15 additions & 0 deletions mlir/test/Dialect/Linalg/match-ops-interpreter.mlir
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Expand Up @@ -996,6 +996,21 @@ module attributes { transform.target_tag = "start_here" } {
} -> tensor<40x10x50x15xf32>
return %result : tensor<40x10x50x15xf32>
}

func.func @generic_min(%arg0: tensor<1x7x4xf32>, %arg1: tensor<4xf32>, %arg2: tensor<1x1x4xf32>) {
linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1 * 2 + d3 * 2, d2)>,
affine_map<(d0, d1, d2, d3) -> (d3)>,
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel", "reduction"]}
ins(%arg0, %arg1 : tensor<1x7x4xf32>, tensor<4xf32>)
outs(%arg2 : tensor<1x1x4xf32>) {
^bb0(%in: f32, %in_1: f32, %out: f32):
%5 = arith.minimumf %out, %in : f32
linalg.yield %5 : f32
} -> tensor<1x1x4xf32>
return
}
}

// -----
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