🐛 [Bug] torch.ops.aten.remainder.Scalar seems not working with big int

[sean-xiang-applovin]

Bug Description

torch.ops.aten.remainder.Scalar seems to return fmod result when input number is big

To Reproduce

save it and run the script below

import torch
import torch.nn as nn

a = torch.tensor([[5950571286963681280]]).cuda()
example_args = (a,)


class ToyModel(nn.Module):
    def __init__(self):
        super(ToyModel, self).__init__()

    def forward(self, x):
        return torch.remainder(x, 196613)


model = ToyModel().eval().cuda()

with torch.no_grad():
    ep = torch.export.export(model, args=example_args)

from torch_tensorrt.dynamo._compiler import compile as dynamo_compile
from torch_tensorrt import logging as ts_logging

with ts_logging.debug():
    compiled = dynamo_compile(
        exported_program=ep,
        disable_tf32=True,
        inputs=example_args,
        min_block_size=1,
        debug=True,
    )

with torch.no_grad():
    print(compiled(*example_args))

Expected behavior

expected to return result like

tensor([[75722]], device='cuda:0')

however, the printed result is

tensor([[-120891]], device='cuda:0')

my full execution log is
remainder_error.log

Environment

Build information about Torch-TensorRT can be found by turning on debug messages

• Torch-TensorRT Version (e.g. 1.0.0): 10.1.0
• PyTorch Version (e.g. 1.0): 2.4.1+cu124
• CPU Architecture: x86_64
• OS (e.g., Linux): linux
• How you installed PyTorch (conda, pip, libtorch, source): pip
• Build command you used (if compiling from source):
• Are you using local sources or building from archives:
• Python version: 3.11.9
• CUDA version: 12.6
• GPU models and configuration: nvidia L4
• Any other relevant information:

Additional context

[sean-xiang-applovin]

BTW,

1. the converted version of torch.ops.aten.remainder.Scalar seems not even as fast as original ops.
2. it seems torch.ops.aten.remainder.Scalar works with int that is not that big. Not sure if this is caused by int64