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[mlir][scf]Fix scf.forall inlining: add shared outputs #132197

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[mlir][scf]Fix scf.forall inlining: add shared outputs #132197

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7 changes: 5 additions & 2 deletions mlir/lib/Dialect/SCF/Transforms/ForallToFor.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -40,12 +40,15 @@ mlir::scf::forallToForLoop(RewriterBase &rewriter, scf::ForallOp forallOp,
SmallVector<Value> ivs = llvm::map_to_vector(
loopNest.loops, [](scf::ForOp loop) { return loop.getInductionVar(); });

SmallVector<Value> replacementVals = ivs;
for (Value shared : forallOp.getOutputs())
replacementVals.push_back(shared);
Block *innermostBlock = loopNest.loops.back().getBody();
rewriter.eraseOp(forallOp.getBody()->getTerminator());
rewriter.inlineBlockBefore(forallOp.getBody(), innermostBlock,
innermostBlock->getTerminator()->getIterator(),
ivs);
rewriter.eraseOp(forallOp);
replacementVals);
rewriter.replaceOp(forallOp, forallOp.getOutputs());

if (results) {
llvm::move(loopNest.loops, std::back_inserter(*results));
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23 changes: 23 additions & 0 deletions mlir/test/Dialect/SCF/forall-to-for.mlir
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Expand Up @@ -55,3 +55,26 @@ func.func @nested(%ub1: index, %ub2: index, %ub3: index, %ub4: index) {
}
return
}

// -----

func.func @parallel_insert_slice(%arg0: tensor<100xf32>) -> tensor<100xf32> {
%c100 = arith.constant 100 : index
%res = scf.forall (%i) in (%c100) shared_outs(%s = %arg0) -> (tensor<100xf32>) {
%t = "test.foo"() : () -> tensor<100xf32>
scf.forall.in_parallel {
tensor.parallel_insert_slice %t into %s[%i] [100] [1] : tensor<100xf32> into tensor<100xf32>
}
}
return %res : tensor<100xf32>
}
// CHECK-LABEL: func.func @parallel_insert_slice(
// CHECK-SAME: %[[VAL_0:[0-9]+|[a-zA-Z$._-][a-zA-Z0-9$._-]*]]: tensor<100xf32>) -> tensor<100xf32> {
// CHECK: %[[VAL_1:.*]] = arith.constant 100 : index
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index
// CHECK: scf.for %[[VAL_4:.*]] = %[[VAL_2]] to %[[VAL_1]] step %[[VAL_3]] {
// CHECK: %[[VAL_5:.*]] = "test.foo"() : () -> tensor<100xf32>
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Where did the parallel_insert_slice go? I think this pass is incorrect. It should have replaced the parallel_insert_slice with insert_slice.

// CHECK: }
// CHECK: return %[[VAL_0]] : tensor<100xf32>
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@matthias-springer matthias-springer Mar 21, 2025 •
edited
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The result of the scf.for should have been used here. It looks like the generated loop nest does not even have a result/iter_args. The issue that you are fixing here was probably an undocumented limitation of this pass, not necessarily a bug: shared_outs are generally not supported, which made the implementation a bit easier.

But it would be nice to support shared_outs.

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Basically, instead of dropping the terminator of the scf.forall loop, you have to replace it with tensor.insert_slice and yield the result. Also, the loop nest that this pass is generating must have an iter_arg (and result); one per shared_out.

// CHECK: }