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[Relax][Torch] Fixed issues related to sum op when without dim and keep dim #18583

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Merged
tlopex merged 5 commits into from
Dec 13, 2025
Merged

[Relax][Torch] Fixed issues related to sum op when without dim and keep dim #18583

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239aab2
[Relax][Torch] Fix issue sum op when without dim and keep dim
locnd182644 Dec 12, 2025
0019f82
Remove and Rerun
locnd182644 Dec 12, 2025
5432f24
Fix lint error in test_frontend_from_exported_program.py
locnd182644 Dec 13, 2025
51ccf58
Fix test_cross_entropy with sum model fixed
locnd182644 Dec 13, 2025
91e0165
Fix line length lint error
locnd182644 Dec 13, 2025
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10 changes: 6 additions & 4 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 @@ -1628,10 +1628,12 @@ def _std(self, node: fx.Node) -> relax.Var:

def _sum(self, node: fx.Node) -> relax.Var:
args = self.retrieve_args(node)
keepdim = node.kwargs["keepdim"] if "keepdim" in node.kwargs else False
if len(args) == 1:
return self.block_builder.emit(relax.op.sum(args[0], keepdims=keepdim))
return self.block_builder.emit(relax.op.sum(args[0], args[1]))
x = args[0]
dim = args[1] if len(node.args) > 1 else node.kwargs.get("dim", None)
if isinstance(dim, (list, tuple)) and len(dim) == 0:
dim = None
keepdim = args[2] if len(node.args) > 2 else node.kwargs.get("keepdim", False)
return self.block_builder.emit(relax.op.sum(x, dim, keepdims=keepdim))

def _var(self, node: fx.Node) -> relax.Var:
args = self.retrieve_args(node)
Expand Down
48 changes: 42 additions & 6 deletions tests/python/relax/test_frontend_from_exported_program.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4945,6 +4945,14 @@ class Sum(Module):
def forward(self, x):
return torch.sum(x, (2, 1))

class SumKeepDim(Module):
def forward(self, x):
return torch.sum(x, (2, 1), keepdim=True)

class SumWithoutDim(Module):
def forward(self, x):
return torch.sum(x)

@tvm.script.ir_module
class expected1:
@R.function
Expand All @@ -4958,8 +4966,36 @@ def main(
R.output(gv)
return gv

@tvm.script.ir_module
class expected2:
@R.function
def main(
inp_0: R.Tensor((1, 2, 3, 4), dtype="float32")
) -> R.Tuple(R.Tensor((1, 1, 1, 4), dtype="float32")):
with R.dataflow():
lv: R.Tensor((1, 1, 1, 4), dtype="float32") = R.sum(
inp_0, axis=[2, 1], keepdims=True
)
gv: R.Tuple(R.Tensor((1, 1, 1, 4), dtype="float32")) = (lv,)
R.output(gv)
return gv

@tvm.script.ir_module
class expected3:
@R.function
def main(
inp_0: R.Tensor((1, 2, 3, 4), dtype="float32")
) -> R.Tuple(R.Tensor((), dtype="float32")):
with R.dataflow():
lv: R.Tensor((), dtype="float32") = R.sum(inp_0, axis=None, keepdims=False)
gv: R.Tuple(R.Tensor((), dtype="float32")) = (lv,)
R.output(gv)
return gv

example_args = (torch.randn(1, 2, 3, 4, dtype=torch.float32),)
verify_model(Sum(), example_args, {}, expected1)
verify_model(SumKeepDim(), example_args, {}, expected2)
verify_model(SumWithoutDim(), example_args, {}, expected3)


def test_argmax_argmin():
Expand Down Expand Up @@ -7840,7 +7876,7 @@ def forward(self, x):
@tvm.script.ir_module
class Expected1:
@R.function
def main(x: R.Tensor((4, 3), dtype="float32")) -> R.Tuple(R.Tensor((4,), dtype="float32")):
def main(x: R.Tensor((4, 3), dtype="float32")) -> R.Tuple(R.Tensor((), dtype="float32")):
with R.dataflow():
lv: R.Tensor((4, 3), dtype="float32") = R.astype(x, dtype="float32")
lv1: R.Tensor((4, 3), dtype="float32") = R.nn.log_softmax(lv, axis=1)
Expand All @@ -7863,11 +7899,11 @@ def main(x: R.Tensor((4, 3), dtype="float32")) -> R.Tuple(R.Tensor((4,), dtype="
lv12: R.Tensor((4,), dtype="bool") = R.not_equal(
R.const([0, 1, 2, 1], dtype="int64"), R.const(-100, "int64")
)
lv13: R.Tensor((4,), dtype="bool") = R.sum(lv12, axis=[], keepdims=False)
lv14: R.Tensor((4,), dtype="float32") = R.astype(lv13, dtype="float32")
lv15: R.Tensor((4,), dtype="float32") = R.sum(lv11, axis=[], keepdims=False)
lv16: R.Tensor((4,), dtype="float32") = R.divide(lv15, lv14)
gv: R.Tuple(R.Tensor((4,), dtype="float32")) = (lv16,)
lv13: R.Tensor((), dtype="bool") = R.sum(lv12, axis=None, keepdims=False)
lv14: R.Tensor((), dtype="float32") = R.astype(lv13, dtype="float32")
lv15: R.Tensor((), dtype="float32") = R.sum(lv11, axis=None, keepdims=False)
lv16: R.Tensor((), dtype="float32") = R.divide(lv15, lv14)
gv: R.Tuple(R.Tensor((), dtype="float32")) = (lv16,)
R.output(gv)
return gv

Expand Down