Repository: pytorch
Issue: Different Dropout behavior on macOS and Linux
Labels: low priority,triaged,module: random,module: numerical-reproducibility,actionable

### 🐛 Describe the bug

Even when using a fixed random seed, the dropout masks in macOS and Linux are different. Here is some code to reproduce this:

```python
import torch

print(torch.__version__)

t = torch.tensor([
        [1.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.5517, 0.4483, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.3800, 0.3097, 0.3103, 0.0000, 0.0000, 0.0000],
        [0.2758, 0.2460, 0.2462, 0.2319, 0.0000, 0.0000],
        [0.2175, 0.1983, 0.1984, 0.1888, 0.1971, 0.0000]])

torch.manual_seed(123)
dropout = torch.nn.Dropout(0.5)

print(dropout(t))
```

Below are the results. Note that this is consistent across PyTorch versions (I tested 2.1.0 and 2.2.1) and whether the code is run on the CPU or GPU:



## macOS torch 2.1.0, CPU

```
2.1.0
tensor([[2.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.7600, 0.6194, 0.6206, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.4920, 0.4924, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.3966, 0.0000, 0.3776, 0.0000, 0.0000],
        [0.0000, 0.3326, 0.3332, 0.3084, 0.3332, 0.0000]])
```



## macOS torch 2.2.1, CPU

```
tensor([[2.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.7600, 0.6194, 0.6206, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.4920, 0.4924, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.3966, 0.0000, 0.3776, 0.0000, 0.0000],
        [0.0000, 0.3326, 0.3332, 0.3084, 0.3332, 0.0000]])
```


## Google Colab (Linux) torch 2.1.0, CPU


```
2.1.0+cu121
tensor([[2.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.8966, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.0000, 0.6206, 0.0000, 0.0000, 0.0000],
        [0.5516, 0.4920, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.4350, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.3326, 0.0000, 0.0000, 0.0000, 0.0000]])
```

## Google Colab (Linux) torch 2.2.1, CPU

```
2.2.1+cu121
tensor([[2.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.8966, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.0000, 0.6206, 0.0000, 0.0000, 0.0000],
        [0.5516, 0.4920, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.4350, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.3326, 0.0000, 0.0000, 0.0000, 0.0000]])
```


## Google Colab (Linux) torch 2.1.0, CPU

```
2.1.0+cu121

tensor([[2.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.8966, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.7600, 0.6194, 0.0000, 0.0000, 0.0000, 0.0000],
        [0.0000, 0.4920, 0.0000, 0.4638, 0.0000, 0.0000],
        [0.4350, 0.3966, 0.3968, 0.0000, 0.0000, 0.0000],
        [0.3870, 0.3326, 0.3332, 0.3084, 0.3332, 0.0000]], device='cuda:0')
```

### Versions

## MacOS environment

```
Collecting environment information...
PyTorch version: 2.2.1
Is debug build: False
CUDA used to build PyTorch: None
ROCM used to build PyTorch: N/A

OS: macOS 14.3.1 (arm64)
GCC version: Could not collect
Clang version: 12.0.5 (clang-1205.0.22.9)
CMake version: version 3.26.1
Libc version: N/A

Python version: 3.10.12 | packaged by conda-forge | (main, Jun 23 2023, 22:41:52) [Clang 15.0.7 ] (64-bit runtime)
Python platform: macOS-14.3.1-arm64-arm-64bit
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:
Apple M1

Versions of relevant libraries:
[pip3] numpy==1.26.0
[pip3] torch==2.2.1
[conda] numpy                     1.26.0                   pypi_0    pypi
[conda] torch                     2.2.1                    pypi_0    pypi
```

## Linux environment

```
Collecting environment information...
PyTorch version: 2.1.0+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
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: 14.0.0-1ubuntu1.1
CMake version: version 3.27.9
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-6.1.58+-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: Tesla V100-SXM2-16GB
Nvidia driver version: 535.104.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.6
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.6
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):                             8
On-line CPU(s) list:                0-7
Vendor ID:                          GenuineIntel
Model name:                         Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family:                         6
Model:                              79
Thread(s) per core:                 2
Core(s) per socket:                 4
Socket(s):                          1
Stepping:                           0
BogoMIPS:                           4399.99
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 rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor:                  KVM
Virtualization type:                full
L1d cache:                          128 KiB (4 instances)
L1i cache:                          128 KiB (4 instances)
L2 cache:                           1 MiB (4 instances)
L3 cache:                           55 MiB (1 instance)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-7
Vulnerability Gather data sampling: Not affected
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 rstack overflow: Not affected
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==1.25.2
[pip3] torch==2.1.0+cu121
[pip3] torchaudio==2.1.0+cu121
[pip3] torchdata==0.7.0
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.16.0
[pip3] torchvision==0.16.0+cu121
[pip3] triton==2.1.0
[conda] Could not collect
```

cc @pbelevich