Failed to convert model that has LeakyReLU to ONNX
Labels: module: onnx, triaged

### 🐛 Describe the bug

I want to convert this model to ONNX.

```
class VGG(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 64, 3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(),
            nn.Conv2d(64, 64, 3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.LeakyReLU(),
            nn.MaxPool2d(2, 2),
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(64, 128, 3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(),
            nn.Conv2d(128, 128, 3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.LeakyReLU(),
            nn.MaxPool2d(2, 2),
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(128, 256, 3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(),
            nn.Conv2d(256, 256, 3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(),
            nn.Conv2d(256, 256, 3, stride=1, padding=1),
            nn.BatchNorm2d(256),
            nn.LeakyReLU(),
            nn.MaxPool2d(2, 2),
        )
        self.conv4 = nn.Sequential(
            nn.Conv2d(256, 512, 3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(),
            nn.Conv2d(512, 512, 3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(),
            nn.Conv2d(512, 512, 3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(),
            nn.MaxPool2d(2, 2),
        )
        self.conv5 = nn.Sequential(
            nn.Conv2d(512, 512, 3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(),
            nn.Conv2d(512, 512, 3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(),
            nn.Conv2d(512, 512, 3, stride=1, padding=1),
            nn.BatchNorm2d(512),
            nn.LeakyReLU(),
            nn.MaxPool2d(2, 2),
        )
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.fc = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096), nn.LeakyReLU(True), nn.Linear(4096, 512)
        )

    def forward(self, x):
        x = x.float()
        x = self.conv1(x)
        x = self.conv2(x)
        x = self.conv3(x)
        x = self.conv4(x)
        x = self.conv5(x)
        x = self.avgpool(x)
        x = x.view(-1, 512 * 7 * 7)
        x = self.fc(x)
        return x

```

The command I used:

```
torch.onnx.export(model,               # model being run
                  x,                         # model input (or a tuple for multiple inputs)
                  "face_comparison.onnx",    # where to save the model (can be a file or file-like object)
                  export_params=False,        # store the trained parameter weights inside the model file
                  opset_version=16,          # the ONNX version to export the model to
                  #do_constant_folding=True,  # whether to execute constant folding for optimization
                  input_names = ['input'],   # the model's input names
                  output_names = ['output'], # the model's output names
)
```

And I got this:

```

RuntimeError: 0 INTERNAL ASSERT FAILED at "../torch/csrc/jit/ir/alias_analysis.cpp":615, please report a bug to PyTorch. We don't have an op for aten::leaky_relu but it isn't a special case.  Argument types: Tensor, bool, 

Candidates:
	aten::leaky_relu(Tensor self, Scalar negative_slope=0.01) -> Tensor
	aten::leaky_relu.out(Tensor self, Scalar negative_slope=0.01, *, Tensor(a!) out) -> Tensor(a!)
```

### Versions

```
Collecting environment information...
PyTorch version: 2.0.1+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
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: 10.0.0-4ubuntu1 
CMake version: version 3.25.2
Libc version: glibc-2.31

Python version: 3.10.12 (main, Jun  7 2023, 12:45:35) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.15.109+-x86_64-with-glibc2.31
Is CUDA available: False
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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:                   46 bits physical, 48 bits virtual
CPU(s):                          2
On-line CPU(s) list:             0,1
Thread(s) per core:              2
Core(s) per socket:              1
Socket(s):                       1
NUMA node(s):                    1
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           79
Model name:                      Intel(R) Xeon(R) CPU @ 2.20GHz
Stepping:                        0
CPU MHz:                         2199.998
BogoMIPS:                        4399.99
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       32 KiB
L1i cache:                       32 KiB
L2 cache:                        256 KiB
L3 cache:                        55 MiB
NUMA node0 CPU(s):               0,1
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Mitigation; PTE Inversion
Vulnerability Mds:               Vulnerable; SMT Host state unknown
Vulnerability Meltdown:          Vulnerable
Vulnerability Mmio stale data:   Vulnerable
Vulnerability Retbleed:          Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1:        Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers
Vulnerability Spectre v2:        Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Vulnerable
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 rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.22.4
[pip3] torch==2.0.1+cu118
[pip3] torchaudio==2.0.2+cu118
[pip3] torchdata==0.6.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.15.2
[pip3] torchvision==0.15.2+cu118
[pip3] triton==2.0.0
[conda] Could not collect
```