Repository: pytorch
Issue: The "step unsupported" graph break will make dynamo can't completely trace code after break
Labels: triaged,oncall: pt2,module: dynamo,module: graph breaks

### 🐛 Describe the bug

When executing the following code:
```python
import torch


class MyExp(torch.autograd.Function):
    @staticmethod
    def forward(ctx, i):
        result = i.exp()
        ctx.save_for_backward(result)
        return result

    @staticmethod
    def backward(ctx, grad_output):
        result, = ctx.saved_tensors
        return grad_output * result


class MyModule(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv = torch.nn.Conv2d(64, 64, 1,  bias=False)

    def forward(self, x):
        y = self.conv(x)
        z = torch.relu(y)
        # the assert here is just used to cause a graph break, does not make sense.
        assert MyExp != None, "failed to import MyExp"
        loss = z.pow(2.0).sum()
        return loss


model = MyModule()
compiled_model = torch.compile(model, backend="inductor")

input = torch.randn([1, 64, 32, 32])
loss = compiled_model(input)
print("done")
```
I expected the code before/after assertion will be traced into two separated fx graph, because the assert caused an error "torch._dynamo.exc.Unsupported: comparison AutogradFunctionVariable() <built-in function is_not> ConstantVariable(NoneType)", so a graph break happens.

But actually I got **only one** fx graph, which contains the ops before assertion. As to the code (loss = z.pow(2.0).sum()) after assertion, it's not traced by dynamo.

```
===== __compiled_fn_0 =====
<eval_with_key>.0 class GraphModule(torch.nn.Module):
   def forward(self, L_x_ : torch.Tensor):
       l_x_ = L_x_
       
       # File: /home/quyang/my_test/experiments/test_graph_break/test_step_unsupported_no_func.py:23, code: y = self.conv(x)
       y = self.L__self___conv(l_x_);  l_x_ = None
       
       # File: /home/quyang/my_test/experiments/test_graph_break/test_step_unsupported_no_func.py:24, code: z = torch.relu(y)
```

But when I change the code to call a function, the function can be correctly traced.
```python
def loss_func(x):
    return x.pow(2.0).sum()

class MyModule(torch.nn.Module):
    def forward(self, x):
        y = self.conv(x)
        z = torch.relu(y)
        assert MyExp != None, "failed to import MyExp"
        loss = loss_func(z)
        return loss
```

```
===== __compiled_fn_0 =====
<eval_with_key>.0 class GraphModule(torch.nn.Module):
   def forward(self, L_x_ : torch.Tensor):
       l_x_ = L_x_
       
       # File: /home/quyang/qnpu/pt/src/pytorch-integration/experiments/test_step_unsupported/test.py:27, code: y = self.conv(x)
       y = self.L__self___conv(l_x_);  l_x_ = None
       
       # File: /home/quyang/qnpu/pt/src/pytorch-integration/experiments/test_step_unsupported/test.py:28, code: z = torch.relu(y)
       z = torch.relu(y);  y = None
       return (z,)

===== __compiled_fn_1 =====
<eval_with_key>.40 class GraphModule(torch.nn.Module):
   def forward(self, L_x_ : torch.Tensor):
       l_x_ = L_x_
       
       # File: /home/quyang/qnpu/pt/src/pytorch-integration/experiments/test_step_unsupported/test.py:18, code: return x.pow(2.0).sum()
       pow_1 = l_x_.pow(2.0);  l_x_ = None
       sum_1 = pow_1.sum();  pow_1 = None
       return (sum_1,)
```
But if there are still other non-function-call codes after the function calls, that codes still can't be traced:
```python
    def forward(self, x):
        y = self.conv(x)
        z = torch.relu(y)
        assert MyExp != None, "failed to import MyExp"
        loss = loss_func(z)
        loss = torch.sigmoid(loss)/2.0 # can't be traced to fx graph
        return loss
```


### Error logs

_No response_

### Minified repro

_No response_

### Versions

Collecting environment information...
PyTorch version: 2.4.0.dev20240429+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.0-102-generic-x86_64-with-glibc2.35
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
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
Address sizes:                      43 bits physical, 48 bits virtual
Byte Order:                         Little Endian
CPU(s):                             12
On-line CPU(s) list:                0-11
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) Gold 6132 CPU @ 2.60GHz
CPU family:                         6
Model:                              85
Thread(s) per core:                 1
Core(s) per socket:                 6
Socket(s):                          2
Stepping:                           0
BogoMIPS:                           5187.81
Flags:                              fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon nopl xtopology tsc_reliable nonstop_tsc cpuid tsc_known_freq pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 invpcid avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xsaves arat pku ospke md_clear flush_l1d arch_capabilities
Virtualization:                     VT-x
Hypervisor vendor:                  VMware
Virtualization type:                full
L1d cache:                          384 KiB (12 instances)
L1i cache:                          384 KiB (12 instances)
L2 cache:                           12 MiB (12 instances)
L3 cache:                           38.5 MiB (2 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-11
Vulnerability Gather data sampling: Unknown: Dependent on hypervisor status
Vulnerability Itlb multihit:        KVM: Mitigation: VMX disabled
Vulnerability L1tf:                 Mitigation; PTE Inversion; VMX flush not necessary, SMT disabled
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT Host state unknown
Vulnerability Retbleed:             Mitigation; IBRS
Vulnerability Spec rstack overflow: Not affected
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; IBRS, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] pytorch-triton==3.0.0+45fff310c8
[pip3] torch==2.4.0.dev20240429+cu118
[pip3] torchaudio==2.2.0.dev20240429+cu118
[pip3] torchvision==0.19.0.dev20240429+cu118
[conda] Could not collect

cc @ezyang @msaroufim @bdhirsh @anijain2305 @chauhang @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng