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[Relax][Frontend][Torch] Fix parsing error when input dimension of unbind is 1 #18351

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merged 2 commits into from
Sep 28, 2025
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[Relax][Frontend][Torch] Fix parsing error when input dimension of unbind is 1 #18351

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[Relax][Frontend][Torch] Fix parsing error when input dimension of un…
eaten-cake Sep 28, 2025
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reformat code
eaten-cake Sep 28, 2025
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10 changes: 7 additions & 3 deletions python/tvm/relax/frontend/torch/base_fx_graph_translator.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1275,9 +1275,13 @@ def _unbind(self, node: fx.Node) -> relax.Var:
dim = node.args[1] if len(node.args) > 1 else node.kwargs.get("dim", 0)
assert isinstance(dim, int), "Expected 2nd argument of unbind as int"
selections = self.shape_of(x)[dim].value
ret, split = [], self.block_builder.emit(relax.op.split(x, selections, dim))
for i in range(selections):
ret.append(self.block_builder.emit(relax.op.squeeze(split[i], axis=dim)))
ret = []
if selections == 1:
ret.append(self.block_builder.emit(relax.op.squeeze(x, axis=dim)))
else:
split = self.block_builder.emit(relax.op.split(x, selections, dim))
for i in range(selections):
ret.append(self.block_builder.emit(relax.op.squeeze(split[i], axis=dim)))
return self.block_builder.emit(relax.Tuple(ret))

########## Statistical ##########
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16 changes: 16 additions & 0 deletions tests/python/relax/test_frontend_from_exported_program.py
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Expand Up @@ -3251,9 +3251,25 @@ def main(
R.output(gv)
return gv

@tvm.script.ir_module
class expected3:
@R.function
def main(
data: R.Tensor((3, 1, 3), dtype="float32")
) -> R.Tuple(R.Tensor((3, 3), dtype="float32")):
with R.dataflow():
lv: R.Tensor((3, 3), dtype="float32") = R.squeeze(data, axis=[1])
lv1: R.Tuple(R.Tensor((3, 3), dtype="float32")) = (lv,)
lv2: R.Tensor((3, 3), dtype="float32") = lv1[0]
gv: R.Tuple(R.Tensor((3, 3), dtype="float32")) = (lv2,)
R.output(gv)
return gv

example_args = (torch.randn(3, 3, 10, 10, dtype=torch.float32),)
verify_model(Unbind1(), example_args, {}, expected1)
verify_model(Unbind2(), example_args, {}, expected2)
single_dim_args = (torch.randn(3, 1, 3, dtype=torch.float32),)
verify_model(Unbind2(), single_dim_args, {}, expected3)


def test_interpolate():
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