🐛 [Bug] Shape mismatch bug using `view` in FX aten path

[gs-olive]

Bug Description

When compiling the small model below via the FX aten path, an error is encountered during shape comparisons.

def forward(self, x):
    y = x.view(-1, 25)
    return y

ERROR:

  File "~/TensorRT/py/torch_tensorrt/fx/lower.py", line 86, in compile
    return lowerer(module, input)
  File "~/TensorRT/py/torch_tensorrt/fx/lower.py", line 316, in __call__
    return do_lower(module, inputs)
  File "~/TensorRT/py/torch_tensorrt/fx/passes/pass_utils.py", line 156, in pass_with_validation
    raise e
  File "~/TensorRT/py/torch_tensorrt/fx/passes/pass_utils.py", line 142, in pass_with_validation
    torch.testing.assert_close(x, y, **kwargs2)
  File "/usr/local/lib/python3.8/dist-packages/torch/testing/_comparison.py", line 1514, in assert_close
    raise error_metas[0].to_error(msg)
AssertionError: Pass <function Lowerer.__call__.<locals>.do_lower at 0x7f4e8db0b790> failed correctness check due at output 0:
The values for attribute 'shape' do not match: torch.Size([5, 25]) != torch.Size([5, 1, 25]).

To Reproduce

Steps to reproduce the behavior:

1. Run the code sample below

import torch
import torch_tensorrt
    

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

    def forward(self, x):
        y = x.view(-1, 25)
        return y

def main():
    model = Sample().cuda().eval()
    input_data = torch.zeros((5, 5, 5), dtype=torch.float, device="cuda:0")
    out_torch = model(input_data)

    mod = torch_tensorrt.fx.compile(model, [input_data],
                                    lower_precision=torch_tensorrt.fx.utils.LowerPrecision.FP32,
                                    min_acc_module_size=1, is_aten=True)

    out_trt = mod(input_data)
    print(out_trt)

main()

Expected behavior

The model should compile

Environment

• Torch-TensorRT Version (e.g. 1.0.0): ad5e764
• PyTorch Version (e.g. 1.0): 2.1.0.dev20230314+cu117

Additional Information

Related to #1673 and #1708

[peri044]

This is fixed when we use explicit batch = True in fx.compile. fx_ts_compat.compile works by default since explicit batch is on by default.