Repository: pytorch
Issue: torch.compile for division gives different numeric output vs eager mode division: torch.tensor/torch.tensor
Labels: high priority,triaged,oncall: pt2,module: inductor

### 🐛 Describe the bug

How to reproduce:
```python
import torch
from decimal import Decimal

y = torch.tensor([7.0], device="cuda") # torch.tensor
x = 11.0 # const

@torch.compile
def compiled_divide_v1(x, y):
    return x / y

@torch.compile
def compiled_divide_v2(x: torch.Tensor, y: torch.Tensor):
    return (x / y.to(torch.float64)).to(torch.float32)

# torch.compile with "/"
Decimal(compiled_divide_v1(torch.tensor(x), y).item()) # 1.57142865657806396484375

# const/torch.tensor
Decimal((x / y).item()) # 1.57142865657806396484375

# torch.compile with "/" and casting to torch.float64
Decimal(compiled_divide_v2(x, y).item()) # 1.5714285373687744140625

# torch.div
Decimal(torch.div(x, y).item()) # 1.5714285373687744140625

# true_divide
Decimal(torch.true_divide(torch.tensor(x, device="cuda"), y).item()) # 1.5714285373687744140625

# torch.tensor/torch.tensor
Decimal((torch.tensor(x) / y).item()) # 1.5714285373687744140625

```

It is surprising to see that `torch.compile with "/"` implemented in function `compiled_divide_v1` returns different output numerically (after 1e-6) in comparison to `torch.tensor/torch.tensor`: 1.57142865657806396484375 vs 1.5714285373687744140625.
To make `compiled_divide_v1` return the same output with `torch.tensor/torch.tensor`, I need to use `torch.compile with "/" and casting to torch.float64` as it is done in `compiled_divide_v2`.

I observe that divisions: `compiled_divide_v1`; `const/torch.tensor` are doing division which are not using rounding to nearest wrt the IEEE standard and they return output 1.57142865657806396484375 which is different with
divisions: `compiled_divide_v2`; `torch.div`; `torch.true_divide`; `torch.tensor/torch.tensor`, they are using rounding to nearest wrt the IEEE standard, as it is done in [triton.language.div_rn](https://triton-lang.org/main/python-api/generated/triton.language.div_rn.html) and return output 1.5714285373687744140625.

It is not clear if all above is a bug or a feature, but at least it would be great to have a proper documentation about `torch.true_divide`; `torch.div`; `torch.tensor/torch.tensor`; `const/torch.tensor`; `torch.compile with "/"`. Currently the descriptions of these ops do not mention anything about IEEE float rounding.
For example description of [torch.div](https://pytorch.org/docs/stable/generated/torch.div.html#torch.div); [torch.true_divide](https://pytorch.org/docs/stable/generated/torch.true_divide.html) do not say anything about `rounding to nearest wrt the IEEE standard`. `const/torch.tensor` is not even documented (but it returns different output vs `torch.div`)

Good documentation example is [triton.language.div_rn](https://triton-lang.org/main/python-api/generated/triton.language.div_rn.html) they describe that it uses `rounding to nearest wrt the IEEE standard`.

### Versions

PyTorch version: 2.3.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 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 3.22.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov  6 2024, 20:22:13) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.8.0-49-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.99
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA RTX 3500 Ada Generation Laptop GPU
Nvidia driver version: 550.120
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.1.1
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):                               20
On-line CPU(s) list:                  0-19
Vendor ID:                            GenuineIntel
Model name:                           13th Gen Intel(R) Core(TM) i7-13800H
CPU family:                           6
Model:                                186
Thread(s) per core:                   2
Core(s) per socket:                   14
Socket(s):                            1
Stepping:                             2
CPU max MHz:                          5200.0000
CPU min MHz:                          400.0000
BogoMIPS:                             5836.80
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 vmx 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 ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad 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 user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq tme rdpid movdiri movdir64b fsrm md_clear serialize pconfig arch_lbr ibt flush_l1d arch_capabilities
Virtualization:                       VT-x
L1d cache:                            544 KiB (14 instances)
L1i cache:                            704 KiB (14 instances)
L2 cache:                             11.5 MiB (8 instances)
L3 cache:                             24 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-19
Vulnerability Gather data sampling:   Not affected
Vulnerability Itlb multihit:          Not affected
Vulnerability L1tf:                   Not affected
Vulnerability Mds:                    Not affected
Vulnerability Meltdown:               Not affected
Vulnerability Mmio stale data:        Not affected
Vulnerability Reg file data sampling: Mitigation; Clear Register File
Vulnerability Retbleed:               Not affected
Vulnerability Spec rstack overflow:   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 / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds:                  Not affected
Vulnerability Tsx async abort:        Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==8.9.2.26
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-nccl-cu12==2.20.5
[pip3] nvidia-nvjitlink-cu12==12.5.40
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] onnxruntime==1.18.0
[pip3] optree==0.13.0
[pip3] torch==2.3.1
[pip3] torchaudio==2.3.1
[pip3] torchvision==0.18.1
[pip3] triton==2.3.1


cc @ezyang @gchanan @zou3519 @kadeng @msaroufim @chauhang @penguinwu @voznesenskym @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @yf225 @chenyang78 @muchulee8 @ColinPeppler @amjames @desertfire @aakhundov @BoyuanFeng