Repository: pytorch
Issue: torch.multiprocessing.start_processes is blocking with large input arguments
Labels: module: multiprocessing,triaged,bug

### 🐛 Describe the bug

With small input arguments (<64kb) `start_processes` runs quickly as the processes are launched asynchronously.

When they're large we end up blocking in https://github.com/python/cpython/blob/main/Lib/multiprocessing/popen_spawn_posix.py#L62 when writing to the pipe. The default pipe buffer size is 64kb so larger than that requires the child process to fully start.

https://unix.stackexchange.com/questions/11946/how-big-is-the-pipe-buffer

Repro:
```
import time
from torch.multiprocessing.spawn import start_processes
import os

time.sleep(1)

def trainer(rank):
    print(rank)

if __name__ == '__main__':
    world_size = 10

    start = time.perf_counter()

    args = ["1"*100000]

    ctx = start_processes(
            fn=trainer,
            args=args,
            nprocs=world_size,
            start_method="spawn",
            join=False,
        )

    print(f"Time taken: {time.perf_counter() - start}")
```

### Versions

```
Collecting environment information...
PyTorch version: 2.5.0a0+git21d4c48
Is debug build: False
CUDA used to build PyTorch: 12.2
ROCM used to build PyTorch: N/A

OS: CentOS Stream 9 (x86_64)
GCC version: (GCC) 11.4.1 20231218 (Red Hat 11.4.1-3)
Clang version: Could not collect
CMake version: version 3.26.4
Libc version: glibc-2.34

Python version: 3.10.13 (main, Sep 11 2023, 13:44:35) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.19.0-0_fbk21_hardened_12633_g4db063a1bcb5-x86_64-with-glibc2.34
Is CUDA available: True
CUDA runtime version: 12.2.140
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: 
GPU 0: NVIDIA H100
GPU 1: NVIDIA H100
GPU 2: NVIDIA H100
GPU 3: NVIDIA H100

Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib64/libcudnn.so.8.8.1
/usr/lib64/libcudnn.so.9.1.1
/usr/lib64/libcudnn_adv.so.9.1.1
/usr/lib64/libcudnn_adv_infer.so.8.8.1
/usr/lib64/libcudnn_adv_train.so.8.8.1
/usr/lib64/libcudnn_cnn.so.9.1.1
/usr/lib64/libcudnn_cnn_infer.so.8.8.1
/usr/lib64/libcudnn_cnn_train.so.8.8.1
/usr/lib64/libcudnn_engines_precompiled.so.9.1.1
/usr/lib64/libcudnn_engines_runtime_compiled.so.9.1.1
/usr/lib64/libcudnn_graph.so.9.1.1
/usr/lib64/libcudnn_heuristic.so.9.1.1
/usr/lib64/libcudnn_ops.so.9.1.1
/usr/lib64/libcudnn_ops_infer.so.8.8.1
/usr/lib64/libcudnn_ops_train.so.8.8.1
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:                   52 bits physical, 57 bits virtual
Byte Order:                      Little Endian
CPU(s):                          184
On-line CPU(s) list:             0-183
Vendor ID:                       AuthenticAMD
Model name:                      AMD EPYC 9654 96-Core Processor
CPU family:                      25
Model:                           17
Thread(s) per core:              1
Core(s) per socket:              184
Socket(s):                       1
Stepping:                        1
BogoMIPS:                        4792.79
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 rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr wbnoinvd arat npt lbrv nrip_save tsc_scale vmcb_clean pausefilter pfthreshold v_vmsave_vmload vgif avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm arch_capabilities
Virtualization:                  AMD-V
Hypervisor vendor:               KVM
Virtualization type:             full
L1d cache:                       11.5 MiB (184 instances)
L1i cache:                       11.5 MiB (184 instances)
L2 cache:                        92 MiB (184 instances)
L3 cache:                        2.9 GiB (184 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-183
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:          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
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] flake8==6.1.0
[pip3] flake8-bugbear==23.3.23
[pip3] flake8-coding==1.3.3
[pip3] flake8-comprehensions==3.15.0
[pip3] flake8-executable==2.1.3
[pip3] flake8-logging-format==0.9.0
[pip3] flake8-pyi==23.3.1
[pip3] flake8-simplify==0.19.3
[pip3] mypy==1.10.0
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.26.0
[pip3] optree==0.12.1
[pip3] pytorch-triton==3.0.0+a9bc1a3647
[pip3] torch==2.5.0a0+git21d4c48
[pip3] torchx==0.6.0
[conda] blas                      1.0                         mkl  
[conda] magma-cuda121             2.6.1                         1    pytorch
[conda] mkl                       2023.1.0         h213fc3f_46344  
[conda] mkl-include               2023.2.0            intel_49495    intel
[conda] mkl-service               2.4.0           py310h5eee18b_1  
[conda] mkl-static                2023.2.0            intel_49495    intel
[conda] mkl_fft                   1.3.8           py310h5eee18b_0  
[conda] mkl_random                1.2.4           py310hdb19cb5_0  
[conda] numpy                     1.26.0                   pypi_0    pypi
[conda] numpy-base                1.26.4          py310hb5e798b_0  
[conda] optree                    0.12.1                   pypi_0    pypi
[conda] pytorch-triton            3.0.0+a9bc1a3647          pypi_0    pypi
[conda] torch                     2.5.0a0+git21d4c48           dev_0    <develop>
[conda] torchfix                  0.4.0                    pypi_0    pypi
[conda] torchx                    0.6.0                    pypi_0    pypi
```

cc @VitalyFedyunin