Repository: pytorch
Issue: Output does not match
Labels: needs reproduction,triaged,module: correctness (silent),oncall: pt2,module: pt2 accuracy

### 🐛 Describe the bug

After using torch.compile, I noticed a change in my output:
```
AssertionError: Tensor-likes are not close!

Mismatched elements: 8309 / 967680 (0.9%)
Greatest absolute difference: 0.5625 at index (10, 5, 19, 30) (up to 0.005 allowed)
Greatest relative difference: 30.234093637454983 at index (5, 16, 5, 35) (up to 0.001 allowed)
```
Upon further investigation, I observed the following:
- try twice both without torch.compile, the result is exactly same.
- try twice both with torch.compile, the result is exactly same.
- with and without torch.compile is different, and most of the time Mismatched elements is less than 1%

Additionally, I encountered a related issue discussed at https://github.com/pytorch/pytorch/issues/100075. My network includes GroupNorm (using nn, not functional), and I tested the provided code in my environment (both in Torch 2.1 and 2.2). Interestingly, the reported mismatched elements in that issue were 100%, contrasting with my findings where mismatches were less than 1%.

I am currently unsure of the underlying cause of this issue.


### Error logs

_No response_

### Minified repro

_No response_

### Versions

Collecting environment information...
PyTorch version: 2.1.0a0+32f93b1
Is debug build: False
CUDA used to build PyTorch: 12.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.3 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.27.6
Libc version: glibc-2.35

Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-162-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA GeForce RTX 4090
GPU 1: NVIDIA GeForce RTX 4090
GPU 2: NVIDIA GeForce RTX 4090
GPU 3: NVIDIA GeForce RTX 4090

Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.5
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.5
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):                             56
On-line CPU(s) list:                0-55
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) CPU E5-2680 v4 @ 2.40GHz
CPU family:                         6
Model:                              79
Thread(s) per core:                 2
Core(s) per socket:                 14
Socket(s):                          2
Stepping:                           1
CPU max MHz:                        3300.0000
CPU min MHz:                        1200.0000
BogoMIPS:                           4799.96
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 arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single pti intel_ppin ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a rdseed adx smap intel_pt xsaveopt cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts md_clear flush_l1d
Virtualization:                     VT-x
L1d cache:                          896 KiB (28 instances)
L1i cache:                          896 KiB (28 instances)
L2 cache:                           7 MiB (28 instances)
L3 cache:                           70 MiB (4 instances)
NUMA node(s):                       4
NUMA node0 CPU(s):                  0-6,28-34
NUMA node1 CPU(s):                  7-13,35-41
NUMA node2 CPU(s):                  14-20,42-48
NUMA node3 CPU(s):                  21-27,49-55
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit:        KVM: Mitigation: Split huge pages
Vulnerability L1tf:                 Mitigation; PTE Inversion; VMX conditional cache flushes, SMT vulnerable
Vulnerability Mds:                  Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Meltdown:             Mitigation; PTI
Vulnerability Mmio stale data:      Mitigation; Clear CPU buffers; SMT vulnerable
Vulnerability Retbleed:             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; Retpolines, IBPB conditional, IBRS_FW, STIBP conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Mitigation; Clear CPU buffers; SMT vulnerable

Versions of relevant libraries:
[pip3] numpy==1.22.2
[pip3] onnx==1.14.0
[pip3] pytorch-lightning==2.2.1
[pip3] pytorch-quantization==2.1.2
[pip3] torch==2.1.0a0+32f93b1
[pip3] torch-tensorrt==0.0.0
[pip3] torchdata==0.7.0a0
[pip3] torchmetrics==1.3.2
[pip3] torchtext==0.16.0a0
[pip3] torchvision==0.16.0a0
[pip3] triton==2.1.0+e621604
[conda] Could not collect

cc @ezyang @anijain2305 @chauhang @penguinwu