Unsupported: ONNX export of operator group_norm, unknown input rank.
Labels: module: onnx, triaged, onnx-needs-info

### 🐛 Describe the bug

I try to export my script model to onnx format with `torch.onnx,export`, here's my export function:
```python
def export_onnx_model(model: Net, input_data: Tuple[Any], export_name: str):
    input_names = [
        "actors",
        "actor_idcs",
        "actor_ctrs",
        "g_graph",
        "g_ctrs",
        "g_idcs",
        "g_feats",
        "g_turn",
        "g_control",
        "g_intersect",
        "g_left",
        "g_right",
    ]
    output_names = ["output1"]

    torch.onnx.export(
        model.cuda(),
        input_data,
        f"{export_name}.onnx",
        input_names=input_names,
        output_names=output_names,
        verbose=True,
        opset_version=9,
    )
```
And I get the error msgs as follow:
```
Exception has occurred: SymbolicValueError
Unsupported: ONNX export of operator group_norm, unknown input rank. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues  [Caused by the value 'out defined in (%out : Tensor = onnx::Conv[dilations=[1], group=1, kernel_shape=annotate(List[int], []), pads=[1, 1], strides=[1]](%d_actors.1, %actor_net.groups.0.0.conv1.weight), scope: lane_gcn.lanegcn.Net::/lane_gcn.lanegcn.ActorNet::actor_net/torch.nn.modules.container.Sequential::groups.0/lane_gcn.layers.Res1dDownSample::0/torch.nn.modules.conv.Conv1d::conv1 # /home/zwj/miniconda3/envs/pt2/lib/python3.8/site-packages/torch/nn/modules/conv.py:309:15
)' (type 'Tensor') in the TorchScript graph. The containing node has kind 'onnx::Conv'.] 
    (node defined in   File "/home/zwj/miniconda3/envs/pt2/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 309
                            weight, bias, self.stride,
                            _single(0), self.dilation, self.groups)
        return F.conv1d(input, weight, bias, self.stride,
               ~~~~~~~~ <--- HERE
                        self.padding, self.dilation, self.groups)
Serialized   File "code/__torch__/torch/nn/modules/conv.py", line 29
    weight: Tensor,
    bias: Optional[Tensor]) -> Tensor:
    _1 = torch.conv1d(input, weight, bias, [1], [1], [1])
         ~~~~~~~~~~~~ <--- HERE
    return _1
)

    Inputs:
        #0: d_actors.1 defined in (%d_actors.1 : Float(7, 3, 20, strides=[60, 20, 1], requires_grad=0, device=cuda:0), %d_actor_idcs : Long(7, strides=[1], requires_grad=0, device=cuda:0)[], %d_ctrs : Float(7, 2, strides=[2, 1], requires_grad=0, device=cuda:0)[], %d_graph : Dict(str, Dict(str, Tensor)[]), %g_ctrs : Long(1338, strides=[1], requires_grad=0, device=cuda:0)[], %g_idcs : Long(1380, strides=[1], requires_grad=0, device=cuda:0)[], %g_feats : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %g_turn : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %g_control : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %g_intersect : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %g_left : Dict(str, Tensor), %g_right : Dict(str, Tensor), %actor_net.groups.0.0.conv1.weight : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn1.weight : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn1.bias : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.conv2.weight : Long(1338, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn2.weight : Long(1338, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn2.bias : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.0.weight : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.1.weight : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.1.bias : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.conv1.weight : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn1.weight : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn1.bias : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.conv2.weight : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn2.weight : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn2.bias : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.conv1.weight : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn1.weight : Long(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn1.bias : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.conv2.weight : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn2.weight : Float(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn2.bias : Float(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.0.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.1.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.1.bias : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.conv1.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn1.weight : Float(32, 3, 3, strides=[9, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.conv2.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn2.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn2.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.conv1.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn1.weight : Float(32, 3, 1, strides=[3, 1, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.conv2.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn2.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn2.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.0.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.1.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.conv1.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn1.weight : Float(64, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn1.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.conv2.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn2.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn2.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.norm.weight : Float(64, 32, 1, strides=[32, 1, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.norm.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.norm.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.conv1.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.bn1.weight : Float(128, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.output.bn1.bias : Float(128, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.conv2.weight : Float(128, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.bn2.weight : Float(128, 128, 3, strides=[384, 3, 1], requires_grad=0, device=cuda:0), %actor_net.output.bn2.bias : Float(128, strides=[1], requires_grad=0, device=cuda:0) = prim::Param()
    )  (type 'Tensor')
        #1: actor_net.groups.0.0.conv1.weight defined in (%d_actors.1 : Float(7, 3, 20, strides=[60, 20, 1], requires_grad=0, device=cuda:0), %d_actor_idcs : Long(7, strides=[1], requires_grad=0, device=cuda:0)[], %d_ctrs : Float(7, 2, strides=[2, 1], requires_grad=0, device=cuda:0)[], %d_graph : Dict(str, Dict(str, Tensor)[]), %g_ctrs : Long(1338, strides=[1], requires_grad=0, device=cuda:0)[], %g_idcs : Long(1380, strides=[1], requires_grad=0, device=cuda:0)[], %g_feats : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %g_turn : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %g_control : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %g_intersect : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %g_left : Dict(str, Tensor), %g_right : Dict(str, Tensor), %actor_net.groups.0.0.conv1.weight : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn1.weight : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn1.bias : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.conv2.weight : Long(1338, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn2.weight : Long(1338, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn2.bias : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.0.weight : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.1.weight : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.1.bias : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.conv1.weight : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn1.weight : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn1.bias : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.conv2.weight : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn2.weight : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn2.bias : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.conv1.weight : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn1.weight : Long(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn1.bias : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.conv2.weight : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn2.weight : Float(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn2.bias : Float(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.0.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.1.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.1.bias : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.conv1.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn1.weight : Float(32, 3, 3, strides=[9, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.conv2.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn2.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn2.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.conv1.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn1.weight : Float(32, 3, 1, strides=[3, 1, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.conv2.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn2.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn2.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.0.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.1.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.conv1.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn1.weight : Float(64, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn1.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.conv2.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn2.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn2.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.norm.weight : Float(64, 32, 1, strides=[32, 1, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.norm.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.norm.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.conv1.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.bn1.weight : Float(128, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.output.bn1.bias : Float(128, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.conv2.weight : Float(128, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.bn2.weight : Float(128, 128, 3, strides=[384, 3, 1], requires_grad=0, device=cuda:0), %actor_net.output.bn2.bias : Float(128, strides=[1], requires_grad=0, device=cuda:0) = prim::Param()
    )  (type 'Tensor')
    Outputs:
        #0: out defined in (%out : Tensor = onnx::Conv[dilations=[1], group=1, kernel_shape=annotate(List[int], []), pads=[1, 1], strides=[1]](%d_actors.1, %actor_net.groups.0.0.conv1.weight), scope: lane_gcn.lanegcn.Net::/lane_gcn.lanegcn.ActorNet::actor_net/torch.nn.modules.container.Sequential::groups.0/lane_gcn.layers.Res1dDownSample::0/torch.nn.modules.conv.Conv1d::conv1 # /home/zwj/miniconda3/envs/pt2/lib/python3.8/site-packages/torch/nn/modules/conv.py:309:15
    )  (type 'Tensor')
  File "/home/zwj/Projects/senior_lanegcn/test/test_onnx_trt.py", line 114, in export_onnx_model
    torch.onnx.export(
  File "/home/zwj/Projects/senior_lanegcn/test/test_onnx_trt.py", line 77, in export_model
    export_onnx_model(net, input_data, export_name)
  File "/home/zwj/Projects/senior_lanegcn/test/test_onnx_trt.py", line 150, in <module>
    export_model(ckpt_path, input_data, "lanegcn", export_format="onnx")
torch.onnx.errors.SymbolicValueError: Unsupported: ONNX export of operator group_norm, unknown input rank. Please feel free to request support or submit a pull request on PyTorch GitHub: https://github.com/pytorch/pytorch/issues  [Caused by the value 'out defined in (%out : Tensor = onnx::Conv[dilations=[1], group=1, kernel_shape=annotate(List[int], []), pads=[1, 1], strides=[1]](%d_actors.1, %actor_net.groups.0.0.conv1.weight), scope: lane_gcn.lanegcn.Net::/lane_gcn.lanegcn.ActorNet::actor_net/torch.nn.modules.container.Sequential::groups.0/lane_gcn.layers.Res1dDownSample::0/torch.nn.modules.conv.Conv1d::conv1 # /home/zwj/miniconda3/envs/pt2/lib/python3.8/site-packages/torch/nn/modules/conv.py:309:15
)' (type 'Tensor') in the TorchScript graph. The containing node has kind 'onnx::Conv'.] 
    (node defined in   File "/home/zwj/miniconda3/envs/pt2/lib/python3.8/site-packages/torch/nn/modules/conv.py", line 309
                            weight, bias, self.stride,
                            _single(0), self.dilation, self.groups)
        return F.conv1d(input, weight, bias, self.stride,
               ~~~~~~~~ <--- HERE
                        self.padding, self.dilation, self.groups)
Serialized   File "code/__torch__/torch/nn/modules/conv.py", line 29
    weight: Tensor,
    bias: Optional[Tensor]) -> Tensor:
    _1 = torch.conv1d(input, weight, bias, [1], [1], [1])
         ~~~~~~~~~~~~ <--- HERE
    return _1
)

    Inputs:
        #0: d_actors.1 defined in (%d_actors.1 : Float(7, 3, 20, strides=[60, 20, 1], requires_grad=0, device=cuda:0), %d_actor_idcs : Long(7, strides=[1], requires_grad=0, device=cuda:0)[], %d_ctrs : Float(7, 2, strides=[2, 1], requires_grad=0, device=cuda:0)[], %d_graph : Dict(str, Dict(str, Tensor)[]), %g_ctrs : Long(1338, strides=[1], requires_grad=0, device=cuda:0)[], %g_idcs : Long(1380, strides=[1], requires_grad=0, device=cuda:0)[], %g_feats : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %g_turn : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %g_control : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %g_intersect : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %g_left : Dict(str, Tensor), %g_right : Dict(str, Tensor), %actor_net.groups.0.0.conv1.weight : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn1.weight : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn1.bias : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.conv2.weight : Long(1338, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn2.weight : Long(1338, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn2.bias : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.0.weight : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.1.weight : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.1.bias : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.conv1.weight : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn1.weight : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn1.bias : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.conv2.weight : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn2.weight : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn2.bias : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.conv1.weight : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn1.weight : Long(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn1.bias : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.conv2.weight : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn2.weight : Float(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn2.bias : Float(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.0.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.1.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.1.bias : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.conv1.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn1.weight : Float(32, 3, 3, strides=[9, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.conv2.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn2.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn2.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.conv1.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn1.weight : Float(32, 3, 1, strides=[3, 1, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.conv2.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn2.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn2.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.0.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.1.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.conv1.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn1.weight : Float(64, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn1.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.conv2.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn2.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn2.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.norm.weight : Float(64, 32, 1, strides=[32, 1, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.norm.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.norm.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.conv1.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.bn1.weight : Float(128, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.output.bn1.bias : Float(128, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.conv2.weight : Float(128, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.bn2.weight : Float(128, 128, 3, strides=[384, 3, 1], requires_grad=0, device=cuda:0), %actor_net.output.bn2.bias : Float(128, strides=[1], requires_grad=0, device=cuda:0) = prim::Param()
    )  (type 'Tensor')
        #1: actor_net.groups.0.0.conv1.weight defined in (%d_actors.1 : Float(7, 3, 20, strides=[60, 20, 1], requires_grad=0, device=cuda:0), %d_actor_idcs : Long(7, strides=[1], requires_grad=0, device=cuda:0)[], %d_ctrs : Float(7, 2, strides=[2, 1], requires_grad=0, device=cuda:0)[], %d_graph : Dict(str, Dict(str, Tensor)[]), %g_ctrs : Long(1338, strides=[1], requires_grad=0, device=cuda:0)[], %g_idcs : Long(1380, strides=[1], requires_grad=0, device=cuda:0)[], %g_feats : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %g_turn : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %g_control : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %g_intersect : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %g_left : Dict(str, Tensor), %g_right : Dict(str, Tensor), %actor_net.groups.0.0.conv1.weight : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn1.weight : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn1.bias : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.conv2.weight : Long(1338, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn2.weight : Long(1338, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.bn2.bias : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.0.weight : Long(1380, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.1.weight : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.0.downsample.1.bias : Long(1460, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.conv1.weight : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn1.weight : Long(1456, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn1.bias : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.conv2.weight : Long(1163, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn2.weight : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.0.1.bn2.bias : Long(53, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.conv1.weight : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn1.weight : Long(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn1.bias : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.conv2.weight : Float(1293, 2, strides=[2, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn2.weight : Float(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.bn2.bias : Float(1293, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.0.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.1.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.0.downsample.1.bias : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.conv1.weight : Long(198, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn1.weight : Float(32, 3, 3, strides=[9, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.conv2.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn2.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.1.1.bn2.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.conv1.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn1.weight : Float(32, 3, 1, strides=[3, 1, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.conv2.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn2.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.bn2.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.0.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.1.weight : Float(32, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.0.downsample.1.bias : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.conv1.weight : Float(32, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn1.weight : Float(64, 32, 3, strides=[96, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn1.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.conv2.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn2.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.groups.2.1.bn2.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.norm.weight : Float(64, 32, 1, strides=[32, 1, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.2.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.norm.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.1.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.conv.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.norm.weight : Float(64, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.lateral.0.norm.bias : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.conv1.weight : Float(64, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.bn1.weight : Float(128, 64, 3, strides=[192, 3, 1], requires_grad=0, device=cuda:0), %actor_net.output.bn1.bias : Float(128, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.conv2.weight : Float(128, strides=[1], requires_grad=0, device=cuda:0), %actor_net.output.bn2.weight : Float(128, 128, 3, strides=[384, 3, 1], requires_grad=0, device=cuda:0), %actor_net.output.bn2.bias : Float(128, strides=[1], requires_grad=0, device=cuda:0) = prim::Param()
    )  (type 'Tensor')
    Outputs:
        #0: out defined in (%out : Tensor = onnx::Conv[dilations=[1], group=1, kernel_shape=annotate(List[int], []), pads=[1, 1], strides=[1]](%d_actors.1, %actor_net.groups.0.0.conv1.weight), scope: lane_gcn.lanegcn.Net::/lane_gcn.lanegcn.ActorNet::actor_net/torch.nn.modules.container.Sequential::groups.0/lane_gcn.layers.Res1dDownSample::0/torch.nn.modules.conv.Conv1d::conv1 # /home/zwj/miniconda3/envs/pt2/lib/python3.8/site-packages/torch/nn/modules/conv.py:309:15
    )  (type 'Tensor')
```
I checked `group_norm` operator is supported from `opset=9`, and I try torch version 1.12 and 1.13, all gave the error...
Any ideas to fix that?

### Versions

PyTorch version: 1.13.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.8.10 (default, Jun  4 2021, 15:09:15)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.15.0-67-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090
GPU 4: NVIDIA GeForce RTX 3090
GPU 5: NVIDIA GeForce RTX 3090
GPU 6: NVIDIA GeForce RTX 3090
GPU 7: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.65.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Byte Order:                      Little Endian
Address sizes:                   43 bits physical, 48 bits virtual
CPU(s):                          128
On-line CPU(s) list:             0-127
Thread(s) per core:              2
Core(s) per socket:              32
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       AuthenticAMD
CPU family:                      23
Model:                           49
Model name:                      AMD EPYC 7502 32-Core Processor
Stepping:                        0
Frequency boost:                 enabled
CPU MHz:                         1745.438
CPU max MHz:                     2500.0000
CPU min MHz:                     1500.0000
BogoMIPS:                        5000.36
Virtualization:                  AMD-V
L1d cache:                       2 MiB
L1i cache:                       2 MiB
L2 cache:                        32 MiB
L3 cache:                        256 MiB
NUMA node0 CPU(s):               0-31,64-95
NUMA node1 CPU(s):               32-63,96-127
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] mypy-protobuf==3.3.0
[pip3] numpy==1.23.5
[pip3] torch==1.13.1
[pip3] torch-tensorrt==1.3.0
[pip3] torch2trt==0.2.0
[pip3] torchaudio==2.0.0
[pip3] torchvision==0.15.0
[pip3] triton==2.0.0
[conda] blas                      1.0                         mkl  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.4.0           h06a4308_640  
[conda] mkl-service               2.4.0            py38h7f8727e_0  
[conda] mkl_fft                   1.3.1            py38hd3c417c_0  
[conda] mkl_random                1.2.2            py38h51133e4_0  
[conda] numpy                     1.23.5           py38h14f4228_0  
[conda] numpy-base                1.23.5           py38h31eccc5_0  
[conda] pytorch-cuda              11.7                 h778d358_3    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torch                     1.13.1                   pypi_0    pypi
[conda] torch-tensorrt            1.3.0                    pypi_0    pypi
[conda] torchaudio                2.0.0                py38_cu117    pytorch
[conda] torchtriton               2.0.0                      py38    pytorch
[conda] torchvision               0.15.0               py38_cu117    pytorch