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[mlir][linalg] Handle outer_dims_perm in linalg.pack consumer fusion. #149426

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Merged
merged 1 commit into from
Jul 18, 2025
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[mlir][linalg] Handle outer_dims_perm in linalg.pack consumer fusion. #149426

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9 changes: 8 additions & 1 deletion mlir/lib/Dialect/Linalg/Transforms/TilingInterfaceImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -893,6 +893,13 @@ struct PackOpTiling
SmallVector<OpFoldResult> outerDimOffsets, outerDimSizes;
DenseMap<int64_t, OpFoldResult> dimAndTileMapping =
packOp.getDimAndTileMapping();
SmallVector<int64_t> outerShapeWithoutTranspose(
packOp.getDestType().getShape().take_front(packOp.getSourceRank()));
if (!packOp.getOuterDimsPerm().empty()) {
applyPermutationToVector(
outerShapeWithoutTranspose,
invertPermutationVector(packOp.getOuterDimsPerm()));
}
for (auto dim : llvm::seq<int64_t>(packOp.getSourceRank())) {
if (dimAndTileMapping.count(dim)) {
FailureOr<int64_t> cstTileSize =
Expand All @@ -908,7 +915,7 @@ struct PackOpTiling
// TODO: It could be untiled if the `srcDimSize` is dynamic. It is a
// hard check to determine if a dimension is tiled or not.
int64_t srcDimSize = packOp.getSourceType().getDimSize(dim);
int64_t destDimSize = packOp.getDestType().getDimSize(dim);
int64_t destDimSize = outerShapeWithoutTranspose[dim];
bool isTiled = failed(cstTileSize) ||
ShapedType::isDynamic(srcDimSize) ||
cstTileSize.value() != srcDimSize;
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45 changes: 45 additions & 0 deletions mlir/test/Interfaces/TilingInterface/tile-and-fuse-consumer.mlir
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Expand Up @@ -451,6 +451,51 @@ module attributes {transform.with_named_sequence} {

// -----


func.func @fuse_perfect_tiling_pack_consumer_with_outer_dims_perm(%arg0: tensor<64x32xf32>, %arg1: tensor<64x32xf32>, %arg2: tensor<2x64x16x1xf32>) -> tensor<2x64x16x1xf32> {
%0 = scf.forall (%arg3) = (0) to (32) step (16) shared_outs(%arg4 = %arg1) -> (tensor<64x32xf32>) {
%src = tensor.extract_slice %arg0[0, %arg3] [64, 16] [1, 1] : tensor<64x32xf32> to tensor<64x16xf32>
%dest = tensor.extract_slice %arg4[0, %arg3] [64, 16] [1, 1] : tensor<64x32xf32> to tensor<64x16xf32>
%1 = linalg.exp ins(%src : tensor<64x16xf32>) outs(%dest : tensor<64x16xf32>) -> tensor<64x16xf32>
scf.forall.in_parallel {
tensor.parallel_insert_slice %1 into %arg4[0, %arg3] [64, 16] [1, 1] : tensor<64x16xf32> into tensor<64x32xf32>
}
}
%pack = linalg.pack %0 outer_dims_perm = [1, 0] inner_dims_pos = [1, 0] inner_tiles = [16, 1] into %arg2 : tensor<64x32xf32> -> tensor<2x64x16x1xf32>
return %pack : tensor<2x64x16x1xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["tensor.parallel_insert_slice"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%1 = transform.structured.match ops{["scf.forall"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%consumer, %fused_consumer = transform.test.fuse_consumer %0 in(%1) : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: #[[PACK_RESULT_MAP:.*]] = affine_map<(d0) -> (d0 floordiv 16)>
// CHECK: func.func @fuse_perfect_tiling_pack_consumer_with_outer_dims_perm(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]
// CHECK: %{{.*}}:2 = scf.forall (%[[IV:.*]]) = (0) to (32) step (16)
// CHECK-SAME: shared_outs(%[[FIRST_OUT_ARG:.*]] = %[[ARG1]], %[[PACK_OUT_ARG:.*]] = %[[ARG2]])
// CHECK: %[[ELEM_SRC:.*]] = tensor.extract_slice %[[ARG0]][0, %[[IV]]] [64, 16] [1, 1]
// CHECK: %[[ELEM_DEST:.*]] = tensor.extract_slice %[[FIRST_OUT_ARG]][0, %[[IV]]] [64, 16] [1, 1]
// CHECK: %[[ELEM:.*]] = linalg.exp
// CHECK-SAME: ins(%[[ELEM_SRC]]
// CHECK-SAME: outs(%[[ELEM_DEST]]
// CHECK-DAG: %[[PACK_RESULT_OFFSET:.*]] = affine.apply #[[PACK_RESULT_MAP]](%[[IV]])
// CHECK-DAG: %[[TILED_PACK_DEST:.*]] = tensor.extract_slice %[[PACK_OUT_ARG]][%[[PACK_RESULT_OFFSET]], 0, 0, 0] [1, 64, 16, 1] [1, 1, 1, 1]
// CHECK: %[[PACK:.*]] = linalg.pack %[[ELEM]]
// CHECK-SAME: outer_dims_perm = [1, 0] inner_dims_pos = [1, 0] inner_tiles = [16, 1]
// CHECK-SAME: into %[[TILED_PACK_DEST]]
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[ELEM]] into %[[FIRST_OUT_ARG]][0, %[[IV]]] [64, 16] [1, 1]
// CHECK: tensor.parallel_insert_slice %[[PACK]] into %[[PACK_OUT_ARG]][%[[PACK_RESULT_OFFSET]], 0, 0, 0] [1, 64, 16, 1] [1, 1, 1, 1]

// -----

// It is valid to fuse the pack op in perfect tiling scenario when the dimension
// is dynamic and padding is not needed.

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