torch.compile() bug in AOTAutograd or Dynamo
Labels: triaged, oncall: pt2

### 🐛 Describe the bug

Our training model used to run correctly. And we tired torch.compile() for our model.
In epcoh 4, there is an error. Here is the detail outputs:

  2%|▏         | 1/50 [10:17<8:24:40, 617.98s/it]model saved
Epoch 1/50, Train Loss: 1.0756,           Validation Loss: 1.0851, Duration: 0:10:17.977241, Best Val Epoch: 0
  4%|▍         | 2/50 [19:54<7:54:47, 593.49s/it]model saved
Epoch 2/50, Train Loss: 0.9921,           Validation Loss: 0.9738, Duration: 0:09:36.346815, Best Val Epoch: 1
  6%|▌         | 3/50 [29:30<7:38:43, 585.61s/it]model saved
Epoch 3/50, Train Loss: 0.9042,           Validation Loss: 0.9325, Duration: 0:09:36.220263, Best Val Epoch: 2
  8%|▊         | 4/50 [39:06<7:29:46, 586.65s/it]model saved
Epoch 4/50, Train Loss: 0.8477,           Validation Loss: 0.9207, Duration: 0:09:36.039177, Best Val Epoch: 3

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
[<ipython-input-3-86742d6aee9f>](https://localhost:8080/#) in <cell line: 387>()
    389     model.load_state_dict(torch.load(model_save_path, weights_only=True))
    390 else:
--> 391     train_losses, val_losses = batch_gd(model, criterion, optimizer, epochs)
    392 
    393     plt.figure(figsize=(15,6))

8 frames
[/usr/local/lib/python3.10/dist-packages/torch/_functorch/aot_autograd.py](https://localhost:8080/#) in debug_compiled_function(*args)
   2396                 assert not isinstance(a, Tensor)
   2397             elif not can_require_grad:
-> 2398                 assert not a.requires_grad, format_guard_bug_msg(
   2399                     aot_config,
   2400                     f"{describe_input(i, aot_config)} would not require grad",

AssertionError: At compilation time, graph 8 was compiled under the assumption that parameter/buffer 1 would not require grad, but at runtime this was not the case.  This indicates a guard bug in AOTAutograd or Dynamo, please file a bug to PyTorch.

The relevent code to the issue is we costomized some parameters during the training:
```
def batch_gd(model, criterion, optimizer, epochs):

    train_losses = np.zeros(epochs)
    test_losses = np.zeros(epochs)
    best_test_loss = np.inf
    best_test_epoch = 0
    i = 0
    cont = 0
    for it in tqdm(range(epochs)):

        if (it == 4):
          model.axial_height_1.f_qr.requires_grad = True
          model.axial_height_1.f_kr.requires_grad = True
          model.axial_height_1.f_sve.requires_grad = True
          model.axial_height_1.f_sv.requires_grad = True

          model.axial_width_1.f_qr.requires_grad = True
          model.axial_width_1.f_kr.requires_grad = True
          model.axial_width_1.f_sve.requires_grad = True
          model.axial_width_1.f_sv.requires_grad = True

          model.axial_height_2.f_qr.requires_grad = True
          model.axial_height_2.f_kr.requires_grad = True
          model.axial_height_2.f_sve.requires_grad = True
          model.axial_height_2.f_sv.requires_grad = True

          model.axial_width_2.f_qr.requires_grad = True
          model.axial_width_2.f_kr.requires_grad = True
          model.axial_width_2.f_sve.requires_grad = True
          model.axial_width_2.f_sv.requires_grad = True

        model.train()
```

### Versions

We ran the code on google clobe.
torch version: 2.0.1+cu118.

cc @ezyang @msaroufim @wconstab @bdhirsh @anijain2305