Repository: pytorch
Issue: Generated some code to try to torch compile it, and it fails for the same code when it's torch compiled
Labels: triaged,bug,oncall: pt2,module: fakeTensor

### 🐛 Describe the bug

The code runs fine without torch compile and fails with it.

### Error logs

```
---------------------------------------------------------------------------

AssertionError                            Traceback (most recent call last)

[<ipython-input-10-62eb8aca15c2>](https://localhost:8080/#) in <cell line: 50>()
     48 
     49 # Benchmark the compiled model
---> 50 compiled_time = benchmark_model(compiled_model, x)
     51 print(f"Compiled model time per iteration: {compiled_time:.6f} seconds")
     52 #Compiled model time per iteration: 0.002723 seconds

20 frames

[/tmp/torchinductor_root/xf/cxfiujff7d7xmb256m6kkhbnaffva4zxfz6adttpew65bo3mju4e.py](https://localhost:8080/#) in call(args)
    135         # Source Nodes: [forward_output], Original ATen: [aten.convolution]
    136         buf0 = extern_kernels.convolution(reinterpret_tensor(primals_5, (10, 1, 100), (100, 1, 1), 0), primals_1, stride=(1,), padding=(1,), dilation=(1,), transposed=False, output_padding=(0,), groups=1, bias=None)
--> 137         assert_size_stride(buf0, (10, 16, 100), (1600, 100, 1))
    138         buf1 = empty_strided_cuda((10, 1, 100), (100, 100, 1), torch.float32)
    139         # Source Nodes: [x_flipped], Original ATen: [aten.flip]

AssertionError: expected size 16==16, stride 1==100 at dim=1
```

### Minified repro

```
import torch
import torch.nn as nn
import time

# Define the BidirectionalConv1d module
class BidirectionalConv1d(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0):
        super(BidirectionalConv1d, self).__init__()
        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv2 = nn.Conv1d(in_channels, out_channels, kernel_size, stride, padding)

    def forward(self, x):
        batch_size, time_steps, directions = x.shape
        x = x.permute(0, 2, 1)

        forward_output = self.conv1(x)
        x_flipped = torch.flip(x, [2])
        backward_output = self.conv2(x_flipped)

        output = torch.cat((forward_output, backward_output), dim=1)
        return output

# Function to benchmark the model
def benchmark_model(model, inputs, iterations=100):
    torch.cuda.synchronize()
    start_time = time.time()

    for _ in range(iterations):
        outputs = model(inputs)

    torch.cuda.synchronize()
    end_time = time.time()
    return (end_time - start_time) / iterations

# Example usage
batch_size, time_steps, directions = 10, 100, 1
x = torch.randn(batch_size, time_steps, directions).cuda()

model = BidirectionalConv1d(1, 16, kernel_size=3, stride=1, padding=1).cuda()

# Benchmark the uncompiled model
uncompiled_time = benchmark_model(model, x)
print(f"Uncompiled model time per iteration: {uncompiled_time:.6f} seconds")

# Compile the model with torch.jit.script
compiled_model = torch.compile(model)
#Compiled model time per iteration: 0.002723 seconds

# Benchmark the compiled model
compiled_time = benchmark_model(compiled_model, x)
print(f"Compiled model time per iteration: {compiled_time:.6f} seconds")
```

### Versions

CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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:                        46 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               2
On-line CPU(s) list:                  0,1
Vendor ID:                            GenuineIntel
Model name:                           Intel(R) Xeon(R) CPU @ 2.00GHz
CPU family:                           6
Model:                                85
Thread(s) per core:                   2
Core(s) per socket:                   1
Socket(s):                            1
Stepping:                             3
BogoMIPS:                             4000.29
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 mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves arat md_clear arch_capabilities
Hypervisor vendor:                    KVM
Virtualization type:                  full
L1d cache:                            32 KiB (1 instance)
L1i cache:                            32 KiB (1 instance)
L2 cache:                             1 MiB (1 instance)
L3 cache:                             38.5 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0,1
Vulnerability Gather data sampling:   Not affected
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 Reg file data sampling: Not affected
Vulnerability Retbleed:               Vulnerable
Vulnerability Spec rstack overflow:   Not affected
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; BHI: Vulnerable (Syscall hardening enabled)
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Vulnerable

Versions of relevant libraries:
[pip3] numpy==1.25.2
[pip3] torch==2.3.0+cu121
[pip3] torchaudio==2.3.0+cu121
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.18.0
[pip3] torchvision==0.18.0+cu121
[pip3] triton==2.3.0
[conda] Could not collect

cc @ezyang @anijain2305 @chauhang @eellison