🐛 [Bug] RuntimeError: linear convolution has bias of type <class 'tensorrt_bindings.tensorrt.ITensor'>, Expect Optional[Tensor]

[peri044]

Bug Description

RuntimeError: linear convolution has bias of type <class 'tensorrt_bindings.tensorrt.ITensor'>, Expect Optional[Tensor]

To Reproduce

import torch
import torch_tensorrt
import torchvision.models as models
import timm

class MyModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = torch.nn.Conv2d(3, 16, 3, stride=1, bias=True)
        self.bn = torch.nn.BatchNorm2d(16)
        self.relu = torch.nn.ReLU()

    def forward(self, x):
        out = self.conv(x)
        out = self.bn(out)
        out = self.relu(out)
        return out

model = MyModule().eval().cuda()
input = torch.randn((1, 3, 224, 224)).to("cuda")
compile_spec = {
    "inputs": [
        torch_tensorrt.Input(
            input.shape, dtype=torch.float, format=torch.contiguous_format
        )
    ],
    "enabled_precisions": {torch.float},
    "debug": True,
    "is_aten": True,
    "min_block_size": 5,
}

trt_mod = torch_tensorrt.dynamo.backend.compile(model, **compile_spec)

Expected behavior

Environment

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

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

Additional context

[gs-olive]

This one was fixed in #1972 - could you try the model again with that PR

[github-actions]

This issue has not seen activity for 90 days, Remove stale label or comment or this will be closed in 10 days

[gs-olive]

Fixed on main in recent linear converter implementation