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Float scalars in float64 programs are rounded to float32 by ScalarToTensorPass

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@JacobSzwejbka 已经在做这个了。

开始于 2026年10月27日。

  • #24168 来自 @usmanmateen —— 已合并

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描述

bug module: exir triaged
🐛 Describe the bug

In a float64 program, ops with a Python float operand lose precision after lowering: the scalar is rounded to float32 before the op runs.

import torch
from executorch.exir import to_edge
from executorch.extension.pybindings.portable_lib import _load_for_executorch_from_buffer


class M(torch.nn.Module):
    def forward(self, x):
        return x * 0.1


x = torch.tensor([1.0, 3.0, 123456.789], dtype=torch.float64)
prog = to_edge(torch.export.export(M(), (x,))).to_executorch()
print(_load_for_executorch_from_buffer(prog.buffer).forward((x,))[0].tolist())
print(M()(x).tolist())
[0.10000000149011612, 0.30000000447034836, 12345.679083964951]
[0.1, 0.30000000000000004, 12345.6789]

The same happens for x + 0.1, x - 0.1, x / 0.1, 0.1 - x and torch.deg2rad(x) (which multiplies by π/180): relative errors of about 1e-9 to 1.5e-8, where float64 should be exact to ~1e-16.

ScalarToTensorPass turns the scalar into torch.tensor(value), which is float32 by default. RemoveMixedTypeOperators then casts that constant to float64 with a _to_dim_order_copy, but by then 0.1 is already 0.10000000149011612. Float32 programs are unaffected.

Creating the constant as float64 when the op already has a float64 tensor operand keeps the value exact, and also drops the _to_dim_order_copy from those graphs. Other dtypes keep the current behavior. I'll open a PR with that.

Versions

executorch 1.5.1 (exir/passes/scalar_to_tensor_pass.py is the same on main at 5b14a2f), torch 2.14.1+cpu, Python 3.12, Linux x86_64 (WSL2).

cc @JacobSzwejbka @angelayi

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