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[mlir][scf] Modify the return logic of generateLoopNestUsingForOp (NFC) #159394

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Merged
merged 2 commits into from
Sep 22, 2025
Merged

[mlir][scf] Modify the return logic of generateLoopNestUsingForOp (NFC) #159394

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9fa81b8
when loops is empty, avoid executing yieldTiledValuesFn.
linuxlonelyeagle Sep 17, 2025
9272941
add the case which all tile sizes are 0.
linuxlonelyeagle Sep 18, 2025
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5 changes: 2 additions & 3 deletions mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -455,6 +455,8 @@ static LogicalResult generateLoopNestUsingForOp(
rewriter.setInsertionPointToEnd(loop.getBody());
destinationTensors = loop.getRegionIterArgs();
}
if (loops.empty())
return success();

SmallVector<Value> tiledResults;
SmallVector<SmallVector<OpFoldResult>> resultOffsets, resultSizes;
Expand All @@ -463,9 +465,6 @@ static LogicalResult generateLoopNestUsingForOp(
return rewriter.notifyMatchFailure(
loc, "failed to generate inner tile loop body");
}
if (loops.empty())
return success();

assert(tiledResults.size() == destinationTensors.size() &&
"Number of results of body should be equal to number of iter args");

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20 changes: 20 additions & 0 deletions mlir/test/Dialect/Linalg/transform-op-tile.mlir
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Expand Up @@ -266,3 +266,23 @@ func.func @tile_linalg_matmul(
-> tensor<128x128xf32>
return %0 : tensor<128x128xf32>
}

// -----

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
// expected-error @below {{op expected number of loops to tile (0) to match number of `loops` results (1)}}
%1, %loops = transform.structured.tile_using_for %0 tile_sizes [0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}

func.func @tile_linalg_matmul(
%arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>)
-> tensor<128x128xf32> {
%0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>)
outs(%arg2: tensor<128x128xf32>)
-> tensor<128x128xf32>
return %0 : tensor<128x128xf32>
}