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[torch-frontend] support aten.sort with ascending=True #496

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Feb 20, 2025
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[torch-frontend] support aten.sort with ascending=True #496

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33e58bd
[torch-frontend] support aten.sort with ascending=True
qingyunqu Feb 20, 2025
9542ba0
update version
qingyunqu Feb 20, 2025
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17 changes: 12 additions & 5 deletions frontends/torch-frontend/torch-frontend/lib/Conversion/ConvertTorchToCustomCall.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -613,10 +613,6 @@ class ConvertAtenSortOp : public OpConversionPattern<AtenSortOp> {
return rewriter.notifyMatchFailure(op, "unimplemented: "
"descending is not constant bool");
}
if (descending == false) {
return rewriter.notifyMatchFailure(op,
"unsupported: descending == false");
}
int64_t k = inputType.getDimSize(dim);
if (k == ShapedType::kDynamic) {
return rewriter.notifyMatchFailure(op, "sorted dim must be static");
Expand All @@ -638,7 +634,18 @@ class ConvertAtenSortOp : public OpConversionPattern<AtenSortOp> {

auto customCallOp = rewriter.create<stablehlo::CustomCallOp>(
op->getLoc(), resultTypes, bufferArgs, ArrayRef<NamedAttribute>(attrs));
rewriter.replaceOp(op, customCallOp->getResults());
Value values = customCallOp->getResults()[0];
Value indices = customCallOp->getResults()[1];
if (descending == false) {
values = rewriter.create<stablehlo::ReverseOp>(
op->getLoc(), values.getType(), values,
rewriter.getDenseI64ArrayAttr({dim}));
indices = rewriter.create<stablehlo::ReverseOp>(
op->getLoc(), indices.getType(), indices,
rewriter.getDenseI64ArrayAttr({dim}));
}
rewriter.replaceOp(op, {values, indices});

return success();
}
};
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9 changes: 9 additions & 0 deletions .../torch-frontend/torch-frontend/python/test/test_torchscript/test_byteir_customcall_ops.py
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Expand Up @@ -162,6 +162,15 @@ def test_sort():
inputs = [tu.randn(3, 10)]
custom_test_helper(SortModule(), inputs, "byteir.top_k")

class SortAscendingModule(torch.nn.Module):
def forward(self, x):
return torch.sort(x, dim=-1, descending=False)

@pytest.mark.mhlo_tools
def test_sort_ascending():
inputs = [tu.randn(3, 10)]
custom_test_helper(SortAscendingModule(), inputs, "byteir.top_k")

# ==============================================================================

class MaxDimModule(torch.nn.Module):
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2 changes: 1 addition & 1 deletion frontends/torch-frontend/torch-frontend/python/version.txt
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Original file line number Diff line number Diff line change
@@ -1 +1 @@
1.3.2
1.3.3
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