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[cherry-pick] Unify torch.tensor and torch.ops.aten.scalar_tensor behavior (#158537) #158655

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Merged
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Jul 22, 2025
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[cherry-pick] Unify torch.tensor and torch.ops.aten.scalar_tensor behavior (#158537) #158655

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23 changes: 22 additions & 1 deletion aten/src/ATen/ScalarOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,28 @@ namespace at {
namespace {
template <typename scalar_t>
inline void fill_inplace(Tensor& self, const Scalar& value_scalar) {
auto value = value_scalar.to<scalar_t>();
scalar_t value{};

if constexpr (std::is_same_v<scalar_t, at::Half> ||
std::is_same_v<scalar_t, at::BFloat16> ||
std::is_same_v<scalar_t, at::Float8_e5m2> ||
std::is_same_v<scalar_t, at::Float8_e5m2fnuz> ||
std::is_same_v<scalar_t, at::Float8_e4m3fn> ||
std::is_same_v<scalar_t, at::Float8_e4m3fnuz> ||
std::is_same_v<scalar_t, at::Float8_e8m0fnu>) {
// relaxed float cast: allow inf similar to the torch.tensor constructor
//
// without this, we had the following divergence:
// torch.tensor(1123581321.0, dtype=torch.float16)
// => tensor(inf, dtype=torch.float16)
// torch.ops.aten.scalar_tensor.default(1123581321, dtype=torch.float16)
// => RuntimeError: value cannot be converted to type at::Half without overflow

value = static_cast<scalar_t>(value_scalar.to<double>());
} else {
value = value_scalar.to<scalar_t>();
}

scalar_t* dptr = static_cast<scalar_t*>(self.data_ptr());
*dptr = value;
}
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32 changes: 32 additions & 0 deletions test/dynamo/test_misc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12955,6 +12955,38 @@ def f(actions, n_act, epsilon=0.1):
y = torch.tensor(5)
f(x, y)

def test_dynamic_float_scalar_tensor_coersion(self):
# Minified version of https://github.com/pytorch/pytorch/issues/158376#issuecomment-3079591367
class Foo:
def __init__(self):
self.config = type(
"Config", (), {"pad_val": 1123581321.0, "tolerance": 1e-6}
)

@torch.compile(fullgraph=True)
def forward(self, input):
outputs = torch.where(
torch.abs(input - self.config.pad_val) < self.config.tolerance,
torch.tensor(
self.config.pad_val, dtype=input.dtype, device=input.device
),
torch.tensor(
self.config.pad_val + 1, dtype=input.dtype, device=input.device
),
)
return outputs

foo = Foo()
inputs = torch.randn(3, 4)
result = foo.forward(inputs)

original_pad_val = foo.config.pad_val
foo.config.pad_val += 1.0
result2 = foo.forward(inputs)

# Previously would crash with:
# RuntimeError: value cannot be converted to type at::Half without overflow


devices = ("cuda", "hpu")
instantiate_device_type_tests(MiscTestsDevice, globals(), only_for=devices)
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