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[mlir] computeSliceParameters: Fix offset when m(0) != 0 #122492

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Merged
merged 3 commits into from
Jan 21, 2025
Merged

[mlir] computeSliceParameters: Fix offset when m(0) != 0 #122492

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86b891d
For affine maps where `m(0) != 0`,
mgehre-amd Jan 10, 2025
a01658f
Add assert message; make integer extraction more direct
mgehre-amd Jan 13, 2025
e0201e9
Add [[maybe_unused]] to bool result
mgehre-amd Jan 13, 2025
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11 changes: 10 additions & 1 deletion mlir/lib/Dialect/Linalg/Utils/Utils.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -596,8 +596,17 @@ computeSliceParameters(OpBuilder &builder, Location loc, Value valueToTile,
auto m = map.getSubMap({r});
LLVM_DEBUG(llvm::dbgs() << "computeSliceParameters: submap: " << m << "\n");
IRRewriter rewriter(builder);
OpFoldResult offset = makeComposedFoldedAffineApply(rewriter, loc, m, lbs);
// The offset of the slice is m(lbs) - m(0).
SmallVector<Attribute> zeros(lbs.size(), rewriter.getIndexAttr(0));
SmallVector<Attribute> mAtZero;
[[maybe_unused]] auto res = m.constantFold(zeros, mAtZero);
assert(succeeded(res) && "affine_map must be evaluatable (not symbols)");
int64_t mAtZeroInt =
cast<IntegerAttr>(mAtZero[0]).getValue().getSExtValue();
OpFoldResult offset = makeComposedFoldedAffineApply(
rewriter, loc, m.getResult(0) - mAtZeroInt, lbs);
sliceParams.offsets.push_back(offset);

OpFoldResult closedIntSize =
makeComposedFoldedAffineApply(rewriter, loc, m, subShapeSizes);
// Resulting size needs to be made half open interval again.
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25 changes: 25 additions & 0 deletions mlir/test/Dialect/Linalg/tile-offset.mlir
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@@ -0,0 +1,25 @@
// RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s

// CHECK-LABEL: func @tile_offset
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]:
func.func @tile_offset(%arg0 : tensor<9xf32>) -> tensor<6xf32> {
%empty = tensor.empty() : tensor<6xf32>
// CHECK: scf.for %[[ITER:[a-zA-Z0-9_]+]] =
// CHECK: tensor.extract_slice %[[ARG0]][%[[ITER]]] [6] [1]
%generic = linalg.generic
{indexing_maps = [affine_map<(d0) -> (d0 + 3)>,
affine_map<(d0) -> (d0)>],
iterator_types = ["parallel"]} ins(%arg0: tensor<9xf32>) outs(%empty : tensor<6xf32>) {
^bb0(%in : f32, %out: f32):
linalg.yield %in : f32
} -> tensor<6xf32>
return %generic : tensor<6xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1, %loop = transform.structured.tile_using_for %0 tile_sizes [3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
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