Repository: pytorch
Issue: Inconsistent behavior of gumbel_softmax on cpu vs. gpu
Labels: module: cuda,triaged,module: python frontend

### 🐛 Describe the bug

on cpu:
```python
import torch
torch.manual_seed(0)
torch.cuda.manual_seed(0)
def gumbel_softmax(logits, tau=1, hard=False, dim=1, eps=1e-10):
    # Draw samples from a Gumbel distribution
    uniform_samples = torch.rand_like(logits, dtype=torch.float32)
    gumbel_samples = -torch.log(-torch.log(uniform_samples) + eps)
    # Apply Gumbel-Softmax
    y = logits + gumbel_samples
    # Optionally apply argmax to get the hard sample
    if hard:
        y_hard = torch.zeros_like(y, dtype=torch.float32)
        y_hard.scatter_(1, y.argmax(dim, keepdim=True), 1)
        y = (y_hard - y).detach() + y
    # Scale the result by temperature and return
    return y / tau
logits = torch.tensor([[0.1, 0.2, 0.7], [0.2, 0.5, 0.3]], dtype=torch.float32)
tau = 0.5
hard = True
softmax = gumbel_softmax(logits, tau=tau, hard=hard)
print(softmax)
# tensor([[0., 2., 0.],
#         [0., 0., 2.]])
softmax = gumbel_softmax(logits, tau=1, hard=False)
print(softmax)
# tensor([[0.4381, 2.4135, 0.9407],
#         [0.9801, 0.4484, 0.3921]])
```

on gpu:
```python
import torch
torch.manual_seed(0)
torch.cuda.manual_seed(0)
def gumbel_softmax(logits, tau=1, hard=False, dim=1, eps=1e-10):
    # Draw samples from a Gumbel distribution
    uniform_samples = torch.rand_like(logits, dtype=torch.float32).cuda()
    gumbel_samples = -torch.log(-torch.log(uniform_samples) + eps)
    # Apply Gumbel-Softmax
    y = logits + gumbel_samples
    # Optionally apply argmax to get the hard sample
    if hard:
        y_hard = torch.zeros_like(y, dtype=torch.float32).cuda()
        y_hard.scatter_(1, y.argmax(dim, keepdim=True), 1)
        y = (y_hard - y).detach() + y
    # Scale the result by temperature and return
    return y / tau
logits_cuda = torch.tensor([[0.1, 0.2, 0.7], [0.2, 0.5, 0.3]], dtype=torch.float32).cuda()
tau = 0.5
hard = True
softmax_cuda = gumbel_softmax(logits_cuda, tau=tau, hard=hard)
print(softmax_cuda)
# tensor([[0., 2., 0.],
#         [0., 2., 0.]], device='cuda:0')
softmax_cuda = gumbel_softmax(logits_cuda, tau=1, hard=False)
print(softmax_cuda)
# tensor([[3.6702, 1.6503, 0.9783],
#         [0.0968, 0.4091, 0.3283]], device='cuda:0')
```

`softmax` is not equal to `softmax_cuda`.

### Versions

```bash
Collecting environment information...
PyTorch version: 2.5.0.dev20240708+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.11.0 | packaged by conda-forge | (main, Jan 14 2023, 12:27:40) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.15.0-116-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.4.131
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3060 Ti
Nvidia driver version: 550.76
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.7
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.7
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:                        39 bits physical, 48 bits virtual
Byte Order:                           Little Endian
CPU(s):                               24
On-line CPU(s) list:                  0-23
Vendor ID:                            GenuineIntel
Model name:                           13th Gen Intel(R) Core(TM) i7-13700F
CPU family:                           6
Model:                                183
Thread(s) per core:                   2
Core(s) per socket:                   16
Socket(s):                            1
Stepping:                             1
CPU max MHz:                          5200.0000
CPU min MHz:                          800.0000
BogoMIPS:                             4224.00
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 est tm2 ssse3 sdbg fma cx16 xtpr pdcm 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 vnmi 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 avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr flush_l1d arch_capabilities
Virtualization:                       VT-x
L1d cache:                            640 KiB (16 instances)
L1i cache:                            768 KiB (16 instances)
L2 cache:                             24 MiB (10 instances)
L3 cache:                             30 MiB (1 instance)
NUMA node(s):                         1
NUMA node0 CPU(s):                    0-23
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 and seccomp
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] pytorch-triton==3.0.0+dedb7bdf33
[pip3] torch==2.5.0.dev20240708+cu121
[pip3] torchaudio==2.4.0.dev20240708+cu121
[pip3] torchvision==0.20.0.dev20240708+cu121
[conda] numpy                     1.26.4                   pypi_0    pypi
[conda] pytorch-triton            3.0.0+dedb7bdf33          pypi_0    pypi
[conda] torch                     2.5.0.dev20240708+cu121          pypi_0    pypi
[conda] torchaudio                2.4.0.dev20240708+cu121          pypi_0    pypi
[conda] torchvision               0.20.0.dev20240708+cu121          pypi_0    pypi
```

cc @ptrblck @msaroufim @albanD