Repository: pytorch
Issue: fp16 parity issue with traced code on GPU
Labels: oncall: jit,triaged,module: half,oncall: export

### 🐛 Describe the bug

I have more details in [this colab notebook](https://colab.research.google.com/drive/1lf8RjX2p5F4E3MzN_blbRICughqHkSJP?usp=sharing), but the issue has to do with a specific sequence of operations that are not equivalent between the regular and traced models when running on a Tesla T4 GPU. The colab notebook was run with a T4 GPU runtime and `torch.__version__=="2.2.0.dev20230920+cu118"`.

Here is the code that fails. This does not fail on cpu, or if the operations in `CoordTransform` are simplified.

```python
import torch
print(torch.__version__)

gen = torch.random.manual_seed(123456789)
model_input = torch.rand(512, 512, 2, generator=gen, dtype=torch.float16).to(torch.device("cuda"))

class CoordTransform(torch.nn.Module):
    def forward(self, x_i: torch.Tensor) -> torch.Tensor:
        x = torch.arange(0, 512).to(x_i)
        x = x + x_i[0, :, 0]
        x = x * 0.4
        return x

model = CoordTransform()
ts_model = torch.jit.trace(model, model_input)

res = model(model_input)
res_ts = ts_model(model_input)

torch.testing.assert_close(res, res_ts, rtol=1e-4, atol=1e-1)
```

Output:
```
---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-20-931a9fd6043a> in <cell line: 6>()
      4 res = model(model_input)
      5 res_ts = ts_model(model_input)
----> 6 torch.testing.assert_close(res, res_ts, rtol=1e-4, atol=1e-1)

/usr/local/lib/python3.10/dist-packages/torch/testing/_comparison.py in assert_close(actual, expected, allow_subclasses, rtol, atol, equal_nan, check_device, check_dtype, check_layout, check_stride, msg)
   1518     if error_metas:
   1519         # TODO: compose all metas into one AssertionError
-> 1520         raise error_metas[0].to_error(msg)
   1521 
   1522 

AssertionError: Tensor-likes are not close!

Mismatched elements: 58 / 512 (11.3%)
Greatest absolute difference: 0.125 at index (282,) (up to 0.1 allowed)
Greatest relative difference: 0.00110626220703125 at index (282,) (up to 0.0001 allowed)
```

### Versions

Collecting environment information...
PyTorch version: 2.2.0.dev20230920+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.2 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 3.27.4
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-5.15.120+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 525.105.17
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
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:                        4000.33
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 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

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] pytorch-triton==2.1.0+6e4932cda8
[pip3] torch==2.2.0.dev20230920+cu118
[pip3] torchaudio==2.2.0.dev20230920+cu118
[pip3] torchdata==0.6.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.15.2
[pip3] torchvision==0.17.0.dev20230920+cu118
[pip3] triton==2.0.0
[conda] Could not collect

cc @EikanWang @jgong5 @wenzhe-nrv @sanchitintel