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[FXML-5417] TileUsingInterface: drop unused extract_slice #432

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merged 4 commits into from
Jan 6, 2025
Merged

[FXML-5417] TileUsingInterface: drop unused extract_slice #432

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644c4e9
TileUsingInterface: drop unused extract_slice
cferry-AMD Jan 3, 2025
4f4dd07
Add a test using transform op
cferry-AMD Jan 6, 2025
fa86c86
Remove unused constant
cferry-AMD Jan 6, 2025
3808d36
Merge remote-tracking branch 'origin/feature/fused-ops' into corentin…
cferry-AMD Jan 6, 2025
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6 changes: 6 additions & 0 deletions mlir/lib/Dialect/SCF/Transforms/TileUsingInterface.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1536,6 +1536,12 @@ mlir::scf::tileConsumerAndFuseProducersUsingSCF(
tiledAndFusedOps.insert(tiledAndFusedOp);
}

// Drop the extract_slice if it has been replaced by the tiled producer, and
// is no longer used.
if (worklistItem.candidateSlice->use_empty()) {
rewriter.eraseOp(worklistItem.candidateSlice);
}

if (failed(sliceTracker.insertAndApplyPatterns(worklistCandidates))) {
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return rewriter.notifyMatchFailure(consumer, "cleanup patterns failed");
}
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40 changes: 40 additions & 0 deletions mlir/test/Dialect/Linalg/transform-op-fuse-into-containing.mlir
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Expand Up @@ -651,6 +651,46 @@ module {
}
}

// -----

// This test checks that upon tiling and fusion, Linalg ops that have been tiled
// through fusion and are not used elsewhere are indeed dead code and get
// dropped.

// CHECK-LABEL: func @tile_fuse_drop_dead_producer(
// CHECK-SAME: %[[TA:[0-9a-z]+]]: tensor<10x10xf32>) -> tensor<10x10xf32> {
func.func @tile_fuse_drop_dead_producer(%arg0: tensor<10x10xf32>) -> tensor<10x10xf32> {
%c2f = arith.constant 2.0 : f32

// CHECK-NOT: linalg.generic {{{[^\}]*}}} ins(%[[TA]] : tensor<10x10xf32>) outs(%{{.*}} : tensor<10x10xf32>) {
%empty = tensor.empty() : tensor<10x10xf32>
%0 = linalg.generic {indexing_maps = [affine_map<(i, j) -> (i, j)>, affine_map<(i, j) -> (i, j)>], iterator_types = ["parallel", "parallel"]}
ins(%arg0: tensor<10x10xf32>) outs(%empty: tensor<10x10xf32>) {
^bb0(%a: f32, %b: f32):
%res = arith.addf %a, %c2f : f32
linalg.yield %res : f32
} -> tensor<10x10xf32>

%empty2 = tensor.empty() : tensor<10x10xf32>
// CHECK: scf.for {{.*}} {
// CHECK: scf.for {{.*}} {
// CHECK: linalg.generic
// CHECK: linalg.negf
// CHECK: }
// CHECK: }
%1 = linalg.negf ins(%0 : tensor<10x10xf32>) outs(%empty2 : tensor<10x10xf32>) -> tensor<10x10xf32>

return %1 : tensor<10x10xf32>
}

module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.negf"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%tiled_low, %loop1, %loop2 = transform.structured.fuse %0 [5, 2] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}


////////////////////////////////////////////////////////////////////////////////
// Tests below are expected to fail.
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