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[FXML-5912] Cherry-pick fix for tiling #535

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Merged
merged 1 commit into from
Apr 28, 2025
Merged

[FXML-5912] Cherry-pick fix for tiling #535

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4 changes: 3 additions & 1 deletion mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1119,8 +1119,10 @@ static std::tuple<OpResult, std::optional<OpOperand *>>
getUntiledProducerFromSliceSource(OpOperand *source,
ArrayRef<LoopLikeOpInterface> loops) {
std::optional<OpOperand *> destinationIterArg;
assert(!loops.empty() && "expected non empty loops container");
auto loopIt = loops.rbegin();
while (auto iterArg = dyn_cast<BlockArgument>(source->get())) {
while (loopIt != loops.rend() && isa<BlockArgument>(source->get())) {
auto iterArg = cast<BlockArgument>(source->get());
auto loop = *loopIt;
if (iterArg.getOwner()->getParentOp() != loop)
break;
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54 changes: 54 additions & 0 deletions mlir/test/Interfaces/TilingInterface/tile-and-fuse-using-interface.mlir
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Expand Up @@ -634,3 +634,57 @@ module attributes {transform.with_named_sequence} {
// CHECK: %[[INSERT_SLICE:.+]] = tensor.insert_slice %[[GENERIC]] into %[[ITER_ARG]]
// CHECK: scf.yield %[[INSERT_SLICE]]
// CHECK: return %[[FOR_RESULT]]

// -----

#map = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d3, d2, d1)>
module {
func.func private @tile_one_consumer_using_tile_and_fuse(%arg0: tensor<16x128x48x96xf32>, %arg1: tensor<16x96x48x128xf32>) -> tensor<16x96x48x128xf32> {
%0 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0 : tensor<16x128x48x96xf32>) outs(%arg1 : tensor<16x96x48x128xf32>) {
^bb0(%in: f32, %out: f32):
linalg.yield %in : f32
} -> tensor<16x96x48x128xf32>
return %0 : tensor<16x96x48x128xf32>
}
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1 : !transform.any_op {transform.readonly}) {
%generic = transform.structured.match ops{["linalg.generic"]} in %arg1
: (!transform.any_op) -> !transform.any_op
%a, %loops:4 = transform.structured.fuse %generic {tile_sizes = [1, 16, 16, 16], tile_interchange = [0, 1, 2, 3], apply_cleanup = false}
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}

// CHECK: func.func private @tile_one_consumer_using_tile_and_fuse(%[[VAL_0:.*]]: tensor<16x128x48x96xf32>, %[[VAL_1:.*]]: tensor<16x96x48x128xf32>) -> tensor<16x96x48x128xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 16 : index
// CHECK: %[[VAL_4:.*]] = arith.constant 128 : index
// CHECK: %[[VAL_5:.*]] = arith.constant 48 : index
// CHECK: %[[VAL_6:.*]] = arith.constant 96 : index
// CHECK: %[[VAL_7:.*]] = arith.constant 1 : index
// CHECK: %[[VAL_8:.*]] = scf.for %[[VAL_9:.*]] = %[[VAL_2]] to %[[VAL_3]] step %[[VAL_7]] iter_args(%[[VAL_10:.*]] = %[[VAL_1]]) -> (tensor<16x96x48x128xf32>) {
// CHECK: %[[VAL_11:.*]] = scf.for %[[VAL_12:.*]] = %[[VAL_2]] to %[[VAL_4]] step %[[VAL_3]] iter_args(%[[VAL_13:.*]] = %[[VAL_10]]) -> (tensor<16x96x48x128xf32>) {
// CHECK: %[[VAL_14:.*]] = scf.for %[[VAL_15:.*]] = %[[VAL_2]] to %[[VAL_5]] step %[[VAL_3]] iter_args(%[[VAL_16:.*]] = %[[VAL_13]]) -> (tensor<16x96x48x128xf32>) {
// CHECK: %[[VAL_17:.*]] = scf.for %[[VAL_18:.*]] = %[[VAL_2]] to %[[VAL_6]] step %[[VAL_3]] iter_args(%[[VAL_19:.*]] = %[[VAL_16]]) -> (tensor<16x96x48x128xf32>) {
// CHECK: %[[VAL_20:.*]] = tensor.extract_slice %[[VAL_0]]{{\[}}%[[VAL_9]], %[[VAL_12]], %[[VAL_15]], %[[VAL_18]]] [1, 16, 16, 16] [1, 1, 1, 1] : tensor<16x128x48x96xf32> to tensor<1x16x16x16xf32>
// CHECK: %[[VAL_21:.*]] = tensor.extract_slice %[[VAL_19]]{{\[}}%[[VAL_9]], %[[VAL_18]], %[[VAL_15]], %[[VAL_12]]] [1, 16, 16, 16] [1, 1, 1, 1] : tensor<16x96x48x128xf32> to tensor<1x16x16x16xf32>
// CHECK: %[[VAL_22:.*]] = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[VAL_20]] : tensor<1x16x16x16xf32>) outs(%[[VAL_21]] : tensor<1x16x16x16xf32>) {
// CHECK: ^bb0(%[[VAL_23:.*]]: f32, %[[VAL_24:.*]]: f32):
// CHECK: linalg.yield %[[VAL_23]] : f32
// CHECK: } -> tensor<1x16x16x16xf32>
// CHECK: %[[VAL_25:.*]] = tensor.insert_slice %[[VAL_26:.*]] into %[[VAL_19]]{{\[}}%[[VAL_9]], %[[VAL_18]], %[[VAL_15]], %[[VAL_12]]] [1, 16, 16, 16] [1, 1, 1, 1] : tensor<1x16x16x16xf32> into tensor<16x96x48x128xf32>
// CHECK: scf.yield %[[VAL_25]] : tensor<16x96x48x128xf32>
// CHECK: }
// CHECK: scf.yield %[[VAL_27:.*]] : tensor<16x96x48x128xf32>
// CHECK: }
// CHECK: scf.yield %[[VAL_28:.*]] : tensor<16x96x48x128xf32>
// CHECK: }
// CHECK: scf.yield %[[VAL_29:.*]] : tensor<16x96x48x128xf32>
// CHECK: }
// CHECK: return %[[VAL_30:.*]] : tensor<16x96x48x128xf32>
// CHECK: }
// CHECK: }