torch.cuda.FloatTensor().normal_() generate (partially) different sample on different gpu machines
Labels: module: docs, triaged

### 🐛 torch.cuda.FloatTensor().normal_() returns different results on different GPUs 

So I executed a training script on two different machines and found that the data generated is actually (partially) different. The configs are as follows:

Machine 1: 
* GPU: RTX A4000
* OS: Ubuntu 20.04 LTS
* Python: 3.10.9
* torch version: 2.0.0 with CUDA 11.7
* NVIDIA driver version: 470.161.03

Machine 2:
* GPU: RTX 2070 Super
* OS: Ubuntu 22.04 LTS
* Python: 3.10.9 (same)
* torch version: 2.0.0 with CUDA 11.7 (same)
* NVIDIA driver version: 470.161.03 (same)

Test code for replication:
```
import torch
torch.manual_seed(14)
x = torch.cuda.FloatTensor(1000, 100).normal_(5, 5**.5)
x
```

Machine 1 returns
```
tensor([[2.8731, 5.2727, 2.9889,  ..., 1.6853, 9.6331, 6.6319],
        [3.9968, 3.8097, 5.7328,  ..., 4.4029, 3.9804, 7.4698],
        [6.5552, 4.0857, 4.2315,  ..., 2.7704, 3.5479, 3.3063],
        ...,
        [8.6297, 2.3137, 6.2596,  ..., 7.9358, 4.4697, 2.7475],
        [7.0551, 7.8583, 3.9990,  ..., 4.1139, 8.9315, 4.2042],
        [2.7337, 2.2106, 7.7815,  ..., 8.2207, 6.5395, 4.2604]],
       device='cuda:0')
```

And machine 2 returns:
```
tensor([[2.8731, 5.2727, 2.9889,  ..., 1.6853, 9.6331, 6.6319],
        [3.9968, 3.8097, 5.7328,  ..., 4.4029, 3.9804, 7.4698],
        [6.5552, 4.0857, 4.2315,  ..., 2.7704, 3.5479, 3.3063],
        ...,
        [2.7640, 5.5319, 3.2799,  ..., 3.0997, 2.1668, 5.0841],
        [6.3256, 8.4741, 4.1051,  ..., 2.6423, 7.7107, 3.3614],
        [3.0485, 5.1392, 0.5328,  ..., 3.0785, 4.5691, 3.4167]],
       device='cuda:0')
```

Note that the first 3 rows shown are the same but starting from a certain row the numbers are different.  

Also tested under torch 1.13.1 on both machines, doesn't make a difference. 


### Versions

#### Machine 1
```
PyTorch version: 2.0.0
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.2 LTS (fossa-ditto X84) (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31

Python version: 3.10.9 (main, Mar  8 2023, 10:47:38) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.14.0-1056-oem-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA RTX A4000
GPU 1: NVIDIA RTX A4000
GPU 2: NVIDIA RTX A4000
GPU 3: NVIDIA RTX A4000

Nvidia driver version: 470.161.03
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
Byte Order:                      Little Endian
Address sizes:                   46 bits physical, 48 bits virtual
CPU(s):                          64
On-line CPU(s) list:             0-63
Thread(s) per core:              2
Core(s) per socket:              16
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       GenuineIntel
CPU family:                      6
Model:                           85
Model name:                      Intel(R) Xeon(R) Gold 6226R CPU @ 2.90GHz
Stepping:                        7
CPU MHz:                         2900.000
CPU max MHz:                     3900.0000
CPU min MHz:                     1200.0000
BogoMIPS:                        5800.00
Virtualization:                  VT-x
L1d cache:                       1 MiB
L1i cache:                       1 MiB
L2 cache:                        32 MiB
L3 cache:                        44 MiB
NUMA node0 CPU(s):               0-15,32-47
NUMA node1 CPU(s):               16-31,48-63
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT vulnerable
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 IBRS, IBPB conditional, RSB filling
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Mitigation; TSX disabled
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 pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 invpcid_single intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req pku ospke avx512_vnni md_clear flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==2.0.0
[pip3] torchaudio==2.0.0
[pip3] torchvision==0.15.0
[pip3] triton==2.0.0
[conda] blas                      1.0                         mkl
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.4.0           h06a4308_640
[conda] mkl-service               2.4.0           py310h7f8727e_0
[conda] mkl_fft                   1.3.1           py310hd6ae3a3_0
[conda] mkl_random                1.2.2           py310h00e6091_0
[conda] numpy                     1.23.5          py310hd5efca6_0
[conda] numpy-base                1.23.5          py310h8e6c178_0
[conda] pytorch                   2.0.0           py3.10_cuda11.7_cudnn8.5.0_0    pytorch
[conda] pytorch-cuda              11.7                 h778d358_3    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.0.0               py310_cu117    pytorch
[conda] torchtriton               2.0.0                     py310    pytorch
[conda] torchvision               0.15.0              py310_cu117    pytorc
```

#### Machine 2
```
PyTorch version: 2.0.0
Is debug build: False
CUDA used to build PyTorch: 11.7
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: Could not collect
Libc version: glibc-2.35

Python version: 3.10.9 (main, Mar  8 2023, 10:47:38) [GCC 11.2.0] (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: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 2070 SUPER
Nvidia driver version: 470.161.03
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:                   39 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          6
On-line CPU(s) list:             0-5
Vendor ID:                       GenuineIntel
Model name:                      Intel(R) Core(TM) i5-8400 CPU @ 2.80GHz
CPU family:                      6
Model:                           158
Thread(s) per core:              1
Core(s) per socket:              6
Socket(s):                       1
Stepping:                        10
CPU max MHz:                     4000.0000
CPU min MHz:                     800.0000
BogoMIPS:                        5599.85
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 pni pclmulqdq dtes64 monitor ds_cpl vmx 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 invpcid_single pti ssbd ibrs ibpb stibp tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid mpx rdseed adx smap clflushopt intel_pt xsaveopt xsavec xgetbv1 xsaves dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp md_clear flush_l1d arch_capabilities
Virtualization:                  VT-x
L1d cache:                       192 KiB (6 instances)
L1i cache:                       192 KiB (6 instances)
L2 cache:                        1.5 MiB (6 instances)
L3 cache:                        9 MiB (1 instance)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-5
Vulnerability Itlb multihit:     KVM: Mitigation: VMX disabled
Vulnerability L1tf:              Mitigation; PTE Inversion; VMX conditional cache flushes, SMT disabled
Vulnerability Mds:               Mitigation; Clear CPU buffers; SMT disabled
Vulnerability Meltdown:          Mitigation; PTI
Vulnerability Mmio stale data:   Mitigation; Clear CPU buffers; SMT disabled
Vulnerability Retbleed:          Mitigation; IBRS
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; IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Mitigation; Microcode
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==2.0.0
[pip3] torchaudio==2.0.0
[pip3] torchvision==0.15.0
[pip3] triton==2.0.0
[conda] blas                      1.0                         mkl
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.4.0           h06a4308_640
[conda] mkl-service               2.4.0           py310h7f8727e_0
[conda] mkl_fft                   1.3.1           py310hd6ae3a3_0
[conda] mkl_random                1.2.2           py310h00e6091_0
[conda] numpy                     1.23.5          py310hd5efca6_0
[conda] numpy-base                1.23.5          py310h8e6c178_0
[conda] pytorch                   2.0.0           py3.10_cuda11.7_cudnn8.5.0_0    pytorch
[conda] pytorch-cuda              11.7                 h778d358_3    pytorch
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchaudio                2.0.0               py310_cu117    pytorch
[conda] torchtriton               2.0.0                     py310    pytorch
[conda] torchvision               0.15.0              py310_cu117    pytorch
```

cc @svekars @carljparker