🐛 [Bug] torch.fmod returns a tiny value with the wrong sign when the float quotient rounds up

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Since Nov 10, 2026.

  • #4861 by @usmanmateen — open

Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
70/100
Issue type
Bug
Clarity
Clearly specified
Activity status
Active
Tech stack
python
Domain
compilers

Research direction

Start in impl.elementwise.fmod and its helper trunc_div in py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py, and check how the float quotient is floored before the remainder is computed. Run the reproduction script from the issue on a CUDA machine and compare against torch.fmod for 4.2 / 1.4 and the 0.1 wrapping case. Done means float fmod results match PyTorch in sign and value while integer behaviour is unchanged.

Written by the indexing model from the issue text.

Description

Bug Description

torch.fmod(a, b) on floating point tensors returns a tiny value with the wrong sign when a / b rounds up to a whole number. In float32, torch.fmod(4.2, 1.4) is 1.3999999 in PyTorch and -1.19e-07 in Torch-TensorRT. The result of fmod always has the sign of a, so a negative result for a positive a is never right. It shows up when wrapping values by a period: fmod(x, 0.1) for x = 0.1, 0.2, ..., 10.0 differs from PyTorch for 43 of the 100 values.

impl.elementwise.fmod in py/torch_tensorrt/dynamo/conversion/impl/elementwise/ops.py computes input - trunc_div(input, other) * other, and trunc_div takes FLOOR_DIV of |input| and |other|. For floats, the division is rounded before it's floored. In float32, 4.2 / 1.4 is 2.99999991, which rounds to 3.0, so the quotient comes out as 3 instead of 2 and input - 3 * other is just below zero. PyTorch computes fmod exactly and gets 2.

torch.remainder (%) gives the right result for these inputs.

To Reproduce

import torch
import torch_tensorrt


class Fmod(torch.nn.Module):
    def forward(self, a, b):
        return torch.fmod(a, b)


def trt(model, *args):
    return torch_tensorrt.compile(model.eval().cuda(), ir="dynamo", inputs=list(args), min_block_size=1)(*args)


a = torch.tensor([4.2, -4.2, 0.7, 5.0], device="cuda")
b = torch.tensor([1.4, 1.4, 0.1, 3.0], device="cuda")
print(Fmod()(a, b).tolist())
print(trt(Fmod(), a, b).tolist())

# Wrapping multiples of 0.1 by 0.1
x = torch.arange(1, 101, device="cuda") * 0.1
period = torch.full_like(x, 0.1)
expected, got = Fmod()(x, period), trt(Fmod(), x, period)
print((got - expected).abs().gt(1e-3).sum().item(), "of", x.numel(), "differ")

Output:

[1.3999998569488525, -1.3999998569488525, 0.09999997913837433, 2.0]
[-1.1920928955078125e-07, 1.1920928955078125e-07, -2.2351741790771484e-08, 2.0]
43 of 100 differ

Expected behavior

The same results as PyTorch, which always have the sign of a.

Environment

  • Torch-TensorRT Version: 2.14.0 (pip). fmod and trunc_div are the same on main.
  • PyTorch Version: 2.14.1+cu130
  • CPU Architecture: x86_64
  • OS: Ubuntu 26.04 (WSL2)
  • How you installed PyTorch: pip
  • Python version: 3.12
  • CUDA version: 13.0 (TensorRT 11.1.0.106)
  • GPU models and configuration: NVIDIA GeForce RTX 5060 Ti, driver 596.49

Additional context

I found this with torchparity, a tool I'm writing that runs PyTorch ops through converters and compares the results with eager PyTorch. I have a fix with tests and can open a PR. When the result has the sign opposite to input, it adds |other| back with the sign of input. Integer inputs are unchanged.

#4786 changes trunc_div for large integers and doesn't cover this.

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