CrossEntropyLoss output difference on Windows vs. Linux
Labels: module: windows, triaged

### 🐛 Describe the bug

When calling `torch.nn.CrossEntropyLoss(reduction="none")` on relatively simple inputs, the computed tensor differs slightly on Windows and Linux, respectively. A minimal example which exhibits this behavior can be seen below.

```
import torch

target = torch.arange(10)
predictions = torch.tensor([
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 2, 0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 3, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 4, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 5, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 6, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 7, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 8, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 9],
], dtype=torch.float32)

print(torch.nn.CrossEntropyLoss(reduction="none")(predictions, target))
```

On Windows, this program prints:
```
tensor([2.3026e+00, 1.4612e+00, 7.9661e-01, 3.7024e-01, 1.5258e-01, 5.8874e-02,
        2.2064e-02, 8.1735e-03, 3.0145e-03, 1.1098e-03])
```
 Whereas on Linux, it prints:
```
tensor([2.3026e+00, 1.4612e+00, 7.9661e-01, 3.7024e-01, 1.5258e-01, 5.8874e-02,
        2.2064e-02, 8.1734e-03, 3.0145e-03, 1.1099e-03])
```

Only a few of the elements differ between the two operating systems, and only by a small margin. This behavior was observed on all tested version of PyTorch on both Windows and Linux, including 1.11.0, 1.13.1 and 2.0.0. All computations were performed on CPU.

### Versions

Windows:
```
Collecting environment information...
PyTorch version: 1.11.0+cpu
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: Microsoft Windows 11 Pro
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: N/A

Python version: 3.8.10 (tags/v3.8.10:3d8993a, May  3 2021, 11:48:03) [MSC v.1928 64 bit (AMD64)] (64-bit runtime)
Python platform: Windows-10-10.0.22621-SP0
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture=9
CurrentClockSpeed=2995
DeviceID=CPU0
Family=198
L2CacheSize=5120
L2CacheSpeed=
Manufacturer=GenuineIntel
MaxClockSpeed=2995
Name=11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz
ProcessorType=3
Revision=

Versions of relevant libraries:
[pip3] torch==1.11.0
[conda] Could not collect
```

Linux:
```
Collecting environment information...
PyTorch version: 1.11.0+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
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 3.22.1
Libc version: glibc-2.35

Python version: 3.10.6 (main, Mar 10 2023, 10:55:28) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.19.0-35-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: 
GPU models and configuration: GPU 0: NVIDIA GeForce GTX 1070 Ti
Nvidia driver version: 520.61.05
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:                   43 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          32
On-line CPU(s) list:             0-31
Vendor ID:                       AuthenticAMD
Model name:                      AMD Ryzen Threadripper 2950X 16-Core Processor
CPU family:                      23
Model:                           8
Thread(s) per core:              2
Core(s) per socket:              16
Socket(s):                       1
Stepping:                        2
Frequency boost:                 enabled
CPU max MHz:                     3500,0000
CPU min MHz:                     2200,0000
BogoMIPS:                        6986.07
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sev sev_es
Virtualization:                  AMD-V
L1d cache:                       512 KiB (16 instances)
L1i cache:                       1 MiB (16 instances)
L2 cache:                        8 MiB (16 instances)
L3 cache:                        32 MiB (4 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-31
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          Mitigation; untrained return thunk; SMT vulnerable
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; Retpolines, IBPB conditional, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.21.5
[pip3] torch==1.11.0
[pip3] triton==2.0.0
[conda] Could not collect
```

cc @peterjc123 @mszhanyi @skyline75489 @nbcsm @vladimir-aubrecht