Traced module shows non-deterministic behaviour on CUDA
Labels: oncall: jit

### 🐛 Describe the bug

Traced module produces inconsistent results when running multiple times on GPU even though the use of deterministic algorithms is enforced. The following code snippet demonstrates the issue:

```python
import torch
import numpy as np
from torch import nn
torch.use_deterministic_algorithms(True)


class Foo(nn.Module):
    """Simple CNN model."""
    def __init__(self):
        super().__init__()                
        self.prepro = nn.Sequential(
            nn.Conv2d(3, 24, kernel_size=3, stride=1, padding="same"),
            nn.BatchNorm2d(24),
            nn.ReLU())
        
    def forward(self, x):
        return self.prepro(x)
    
# Random inputs
inputs = torch.randn([1, 3, 352, 224]).cuda()

# Create model and trace it
foo = Foo().eval().cuda()
foo = torch.jit.trace(foo, inputs)

# Add random bias to BatchNorm so it is not zero
params = foo.state_dict()
params['prepro.1.bias'] = torch.randn([24])
foo.load_state_dict(params)

out_foo = foo(inputs)
for i in range(2000):
    out_curr = foo(inputs)
    print(i, torch.equal(out_foo, out_curr))    
    assert torch.equal(out_foo, out_curr)    
```

After a couple of iterations the model output differs from previous runs. Interestingly, the code above works just fine if the ReLU activation is removed from the module.

### Versions

Collecting environment information...
PyTorch version: 2.0.0+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: Could not collect
CMake version: version 3.26.0
Libc version: glibc-2.35

Python version: 3.10.6 (main, Nov 14 2022, 16:10:14) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.17.0-1028-oem-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA RTX A3000 12GB Laptop GPU
Nvidia driver version: 525.85.05
cuDNN version: Could not collect
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):                          24
On-line CPU(s) list:             0-23
Vendor ID:                       GenuineIntel
Model name:                      12th Gen Intel(R) Core(TM) i7-12850HX
CPU family:                      6
Model:                           151
Thread(s) per core:              2
Core(s) per socket:              16
Socket(s):                       1
Stepping:                        2
CPU max MHz:                     3519,2729
CPU min MHz:                     800,0000
BogoMIPS:                        4838.40
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr flush_l1d arch_capabilities
L1d cache:                       640 KiB (16 instances)
L1i cache:                       768 KiB (16 instances)
L2 cache:                        14 MiB (10 instances)
L3 cache:                        25 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-23
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.24.2
[pip3] torch==2.0.0
[pip3] torchvision==0.15.1
[conda] Could not collect


cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel