Repository: pytorch
Issue: Significant performance degradation with multiprocessing in PyTorch 2.x compared to 1.13.1
Labels: module: multiprocessing,triaged

### 🐛 Describe the bug

## Description
When using PyTorch 2.x with multiprocessing (`torch.multiprocessing.Pool`), there is a significant performance degradation (100x) compared to PyTorch 1.13.1 when running on the CPU. The code execution time is much slower in PyTorch 2.x, despite high CPU utilization with all cores being fully utilized. However, the performance difference is not observed when running on the GPU.

## Expected Behavior
The code should perform similarly or better in PyTorch 2.x compared to PyTorch 1.13.1, with high CPU utilization and faster execution times, regardless of the device (CPU or GPU) being used.

## Steps to Reproduce
1. Install PyTorch 1.13.1 and PyTorch 2.x (e.g., 2.2.1) in separate environments.
2. Run the following code snippet in both environments:

```
import torch
from torch.multiprocessing import Pool
import time

DEVICE = 'cpu'
DTYPE = torch.float64


def mp_log_fun(*args):
    arr = torch.rand(14431, dtype=DTYPE, device=DEVICE)
    for i in range(1000):
        arr.log()
    return


if __name__ == '__main__':
    start = time.time()
    with Pool(5) as p:
        p.map(mp_log_fun, list(range(5)))
    print(f'Torch {torch.__version__}. Dtype: {DTYPE}. Device: {DEVICE}. Took: {time.time() - start:.3f} secs.', )
```

---
* Torch 1.13.1+cu117. Dtype: torch.float32. Device: cpu. Took: 0.042 secs.
* Torch 2.2.1+cu121. Dtype: torch.float32. Device: cpu. Took: 20.288 secs.
---
* Torch 1.13.1+cu117. Dtype: torch.float64. Device: cpu. Took: 0.077 secs.
* Torch 2.2.1+cu121. Dtype: torch.float64. Device: cpu. Took: 15.039 secs.
---
* Torch 1.13.1+cu117. Dtype: torch.float32. Device: cuda:0. Took: 3.696 secs.
* Torch 2.2.1+cu121. Dtype: torch.float32. Device: cuda:0. Took: 3.293 secs.
---
* Torch 1.13.1+cu117. Dtype: torch.float64. Device: cuda:0. Took: 3.699 secs.
* Torch 2.2.1+cu121. Dtype: torch.float64. Device: cuda:0. Took: 3.195 secs.

### Versions

Collecting environment information...
PyTorch version: 2.2.1+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 12 (bookworm) (x86_64)
GCC version: (Debian 12.2.0-14) 12.2.0
Clang version: Could not collect
CMake version: version 3.25.1
Libc version: glibc-2.36

Python version: 3.11.8 (main, Mar 14 2024, 15:21:37) [GCC 10.2.1 20210110] (64-bit runtime)
Python platform: Linux-6.1.0-18-amd64-x86_64-with-glibc2.36
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 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090
GPU 3: NVIDIA GeForce RTX 3090

Nvidia driver version: 550.67
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):                             64
On-line CPU(s) list:                0-63
Vendor ID:                          AuthenticAMD
Model name:                         AMD Ryzen Threadripper 3970X 32-Core Processor
CPU family:                         23
Model:                              49
Thread(s) per core:                 2
Core(s) per socket:                 32
Socket(s):                          1
Stepping:                           0
Frequency boost:                    enabled
CPU(s) scaling MHz:                 73%
CPU max MHz:                        3700.0000
CPU min MHz:                        2200.0000
BogoMIPS:                           7400.25
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 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 ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es
Virtualization:                     AMD-V
L1d cache:                          1 MiB (32 instances)
L1i cache:                          1 MiB (32 instances)
L2 cache:                           16 MiB (32 instances)
L3 cache:                           128 MiB (8 instances)
NUMA node(s):                       1
NUMA node0 CPU(s):                  0-63
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 Retbleed:             Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow: Mitigation; safe RET
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 always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:                Not affected
Vulnerability Tsx async abort:      Not affected

Versions of relevant libraries:
[pip3] numpy==1.26.4
[pip3] onnx==1.16.0
[pip3] onnxconverter-common==1.14.0
[pip3] onnxruntime==1.17.1
[pip3] skl2onnx==1.16.0
[pip3] torch==2.2.1
[pip3] triton==2.2.0
[conda] Could not collect

cc @VitalyFedyunin