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[Fix] CommReduce could handle 0-dim data #19683

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[Fix] CommReduce could handle 0-dim data #19683

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4 changes: 3 additions & 1 deletion include/tvm/topi/reduction.h
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Original file line number Diff line number Diff line change
Expand Up @@ -184,7 +184,9 @@ inline Tensor DoCommReduce(const Tensor& data, FReduce func,
inline Tensor CommReduce(const Tensor& data, const ffi::Optional<ffi::Array<int64_t>>& axis,
FReduce func, bool keepdims, bool atleast1d) {
auto ndim = data->shape.size();
TVM_FFI_ICHECK_NE(ndim, 0) << "Cannot reduce a 0 dim Tensor";
if (ndim == 0) {
return topi::identity(data, data->op->name + "_red", kCommReduce);
}
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@gemini-code-assist gemini-code-assist Bot Jun 8, 2026

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medium

When ndim == 0 and atleast1d is true, the output tensor should be at least 1-dimensional (i.e., shape [1]). However, returning topi::identity directly ignores the atleast1d flag and returns a 0-dimensional tensor. We should wrap the identity tensor with topi::expand_dims if atleast1d is enabled.

Suggested change
if (ndim == 0) {
return topi::identity(data, data->op->name + "_red", kCommReduce);
}
if (ndim == 0) {
auto identity = topi::identity(data, data->op->name + "_red", kCommReduce);
return atleast1d ? topi::expand_dims(identity, 0, 1) : identity;
}

auto real_axis = GetRealAxis(static_cast<int>(ndim), axis);
auto target_shape = MakeReduceTargetShape(real_axis, data, keepdims, atleast1d);
return DoCommReduce(data, func, target_shape, real_axis,
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32 changes: 32 additions & 0 deletions tests/python/relax/test_transform_legalize_ops_search_statistical.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1179,5 +1179,37 @@ def main(x: R.Tensor((2, 3, 4, 5), dtype="float32")) -> R.Tensor((3, 4), dtype="
tvm.ir.assert_structural_equal(mod, Expected)


def test_max_zero_dim():
# Reducing a 0-D (scalar) tensor is the identity; it must legalize, not crash.
# Regression test for https://github.com/apache/tvm/issues/19676
# fmt: off
@tvm.script.ir_module
class Max:
@R.function
def main(x: R.Tensor((), "float32")) -> R.Tensor((), "float32"):
gv: R.Tensor((), "float32") = R.max(x)
return gv

@tvm.script.ir_module
class Expected:
@R.function
def main(x: R.Tensor((), dtype="float32")) -> R.Tensor((), dtype="float32"):
gv = R.call_tir(Expected.max, (x,), R.Tensor((), dtype="float32"))
return gv

@T.prim_func(private=True, s_tir=True)
def max(x: T.Buffer((), "float32"), x_red: T.Buffer((), "float32")):
T.func_attr({"tirx.noalias": True})
with T.sblock("x_red"):
vi = T.axis.spatial(1, T.int64(0))
T.reads(x[()])
T.writes(x_red[()])
x_red[()] = x[()]
# fmt: on

mod = LegalizeOps()(Max)
tvm.ir.assert_structural_equal(mod, Expected)


if __name__ == "__main__":
tvm.testing.main()
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