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[mlir] Fix bug in UnPackOp tiling implementation causing infinite loop #113571

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Merged
merged 1 commit into from
Oct 25, 2024
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[mlir] Fix bug in UnPackOp tiling implementation causing infinite loop #113571

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8 changes: 4 additions & 4 deletions mlir/lib/Dialect/Tensor/IR/TensorTilingInterfaceImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -554,13 +554,14 @@ struct UnPackOpTiling
sliceSrcIndices.append(numInnerTiles, zeroAttr);
sliceSrcSizes.append(unpackOp.getMixedTiles());
sliceSrcStrides.append(numInnerTiles, oneAttr);
Value sliceSource =
SmallVector<Operation *> generatedSlices;
ExtractSliceOp sliceSource =
b.create<ExtractSliceOp>(loc, unpackOp.getSource(), sliceSrcIndices,
sliceSrcSizes, sliceSrcStrides);
generatedSlices.push_back(sliceSource);
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I'm realizing that maybe I'm not supposed to add the input operand slices. Should I be doing this?

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According to the the doc, we should return the slices for fusion, so it looks like the change makes sense. Why do you think that we should not do this?

/// - `generatedSlices` contains the list of slices that are generated during
///   tiling. These slices can be used for fusing producers.

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I looked at a few other implementations and they seemed to only add the init argument slices, so I thought there was some context I was missing. I checked some other implementations and they add the input slices as well, though, so I think the ops I looked at happened to be special cases. I think it is good as I had it.


SmallVector<OpFoldResult> destStrides(destRank, oneAttr);
Value sliceDest;
SmallVector<Operation *> generatedSlices;
if (isPerfectTilingCase) {
auto destSliceOp = b.create<ExtractSliceOp>(loc, unpackOp.getDest(),
offsets, sizes, destStrides);
Expand All @@ -571,7 +572,7 @@ struct UnPackOpTiling
unpackOp.getDestType().getElementType());
}

SmallVector<Value> tiledOperands = {sliceSource, sliceDest};
SmallVector<Value> tiledOperands = {sliceSource.getResult(), sliceDest};
for (auto tile : unpackOp.getInnerTiles())
tiledOperands.push_back(tile);

Expand All @@ -586,7 +587,6 @@ struct UnPackOpTiling
auto extractSlice =
b.create<ExtractSliceOp>(loc, tiledUnpackOp->getResult(0),
resultOffsetsFromDest, sizes, destStrides);
generatedSlices.push_back(extractSlice);
return TilingResult{
{tiledUnpackOp}, {extractSlice.getResult()}, generatedSlices};
}
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47 changes: 47 additions & 0 deletions mlir/test/Interfaces/TilingInterface/tile-and-fuse-using-interface.mlir
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Expand Up @@ -587,3 +587,50 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[INSERT_SLICE:.+]] = tensor.insert_slice %[[IF_RESULT]]
// CHECK: scf.yield %[[INSERT_SLICE]]
// CHECK: return %[[FOR_RESULT]]

// -----

func.func @imperfect_unpack_producer_fusion(%source: tensor<1x1x288x8x4xf32>, %dest: tensor<1x2x1152xf32>) -> tensor<1x2x1152xf32> {
%0 = tensor.unpack %source
outer_dims_perm = [0, 1, 2]
inner_dims_pos = [1, 2]
inner_tiles = [8, 4] into %dest
: tensor<1x1x288x8x4xf32> -> tensor<1x2x1152xf32>
%1 = tensor.empty() : tensor<1x2x1152xf32>
%cst = arith.constant 1.0 : f32
%2 = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%0 : tensor<1x2x1152xf32>)
outs(%1 : tensor<1x2x1152xf32>) {
^bb0(%in: f32, %out: f32):
%7 = arith.addf %in, %cst : f32
linalg.yield %7 : f32
} -> tensor<1x2x1152xf32>
return %2 : tensor<1x2x1152xf32>
}

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

// CHECK-LABEL: func @imperfect_unpack_producer_fusion
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x1x288x8x4xf32>
// CHECK-SAME: %[[ARG1:.+]]: tensor<1x2x1152xf32>
// CHECK: %[[FOR_RESULT:.+]] = scf.for{{.*}}iter_args(%[[ITER_ARG:.+]] = {{.*}})
// CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[ARG0]]
// CHECK: %[[UNPACK:.+]] = tensor.unpack %[[SLICE]]
// CHECK-DAG: %[[UNPACK_SLICE:.+]] = tensor.extract_slice %[[UNPACK]]
// CHECK-DAG: %[[INIT_SLICE:.+]] = tensor.extract_slice %[[ITER_ARG]]
// CHECK: %[[GENERIC:.+]] = linalg.generic
// CHECK-SAME: ins(%[[UNPACK_SLICE]]
// CHECK-SAME: outs(%[[INIT_SLICE]]
// CHECK: %[[INSERT_SLICE:.+]] = tensor.insert_slice %[[GENERIC]] into %[[ITER_ARG]]
// CHECK: scf.yield %[[INSERT_SLICE]]
// CHECK: return %[[FOR_RESULT]]
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