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[Torch] Enable dtype inference for operations with list-type operands #4406

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Merged
zjgarvey merged 1 commit into from
Dec 17, 2025
+36 −3
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[Torch] Enable dtype inference for operations with list-type operands #4406

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8 changes: 5 additions & 3 deletions lib/Dialect/Torch/Transforms/SimplifyAbstractInterpCalculationsUtils.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -39,12 +39,14 @@ class FullyUnrollPrimLoopOp : public OpRewritePattern<PrimLoopOp> {
PatternRewriter &rewriter) const override {
Location loc = op->getLoc();
MLIRContext *context = op->getContext();
// Only unroll loops if they are contained in a shape calculate region.
// Only unroll loops if they are contained in a shape or dtype calculate
// regions.
Region *region = op->getParentRegion();
Operation *parentOp = region->getParentOp();
if (!parentOp || !isa<Torch::ShapeCalculateOp>(parentOp))
if (!parentOp ||
!isa<Torch::ShapeCalculateOp, Torch::DtypeCalculateOp>(parentOp))
return rewriter.notifyMatchFailure(
op, "Loop is not contained in a shape calculation region.");
op, "Loop is not contained in a shape or dtype calculation regions.");
if (!op.isForLike())
return rewriter.notifyMatchFailure(op, "Loop is not for-like");
int64_t maxTripCount;
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31 changes: 31 additions & 0 deletions test/Dialect/Torch/simplify-dtype-calculations.mlir
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Expand Up @@ -215,6 +215,37 @@ func.func @promote_dtypes$scalar_scalar_same_category(%arg0: !torch.int, %arg1:

// -----

// CHECK-LABEL: func.func @promote_dtypes$list_tensors
// CHECK: {{.*}} = torch.aten.cat {{.*}} : !torch.list<vtensor>, !torch.int -> !torch.vtensor<*,f32>
func.func @promote_dtypes$list_tensors(%arg0: !torch.vtensor<[1,8,320,384],f32>) -> !torch.vtensor {
%int0 = torch.constant.int 0
%int1 = torch.constant.int 1
%true = torch.constant.bool true
%int-3 = torch.constant.int -3
%0 = torch.prim.ListConstruct %arg0 : (!torch.vtensor<[1,8,320,384],f32>) -> !torch.list<vtensor>
%1 = torch.dtype.calculate {
%2 = torch.aten.cat %0, %int-3 : !torch.list<vtensor>, !torch.int -> !torch.vtensor
torch.dtype.calculate.yield %2 : !torch.vtensor
} dtypes {
%2 = torch.prim.ListConstruct : () -> !torch.list<tuple<int, int>>
torch.prim.Loop %int1, %true, init() {
^bb0(%arg1: !torch.int):
%5 = torch.aten.__getitem__.t %0, %arg1 : !torch.list<vtensor>, !torch.int -> !torch.vtensor
%6 = torch.aten.dim %5 : !torch.vtensor -> !torch.int
%7 = torch.prim.dtype %5 : !torch.vtensor -> !torch.int
%8 = torch.prim.TupleConstruct %6, %7 : !torch.int, !torch.int -> !torch.tuple<int, int>
%9 = torch.aten.append.t %2, %8 : !torch.list<tuple<int, int>>, !torch.tuple<int, int> -> !torch.list<tuple<int, int>>
torch.prim.Loop.condition %true, iter()
} : (!torch.int, !torch.bool) -> ()
%3 = torch.aten.__getitem__.t %2, %int0 : !torch.list<tuple<int, int>>, !torch.int -> !torch.tuple<int, int>
%4:2 = torch.prim.TupleUnpack %3 : !torch.tuple<int, int> -> !torch.int, !torch.int
torch.dtype.calculate.yield.dtypes %4#1 : !torch.int
} : !torch.vtensor
return %1 : !torch.vtensor
}

// -----

// CHECK-LABEL: func.func @refine_dtype$invalid_dtype_for_scalar(
// CHECK: {{.*}} = torch.aten.add {{.*}} -> !torch.number
func.func @refine_dtype$invalid_dtype_for_scalar(%arg0: !torch.int, %arg1: !torch.int) -> !torch.number {
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