[BUG] Getting '[Errno 13] Permission denied' when import hivemind

[yuanluw]

Describe the bug

>>> import hivemind
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/__init__.py", line 1, in <module>
    from hivemind.averaging import DecentralizedAverager
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/averaging/__init__.py", line 1, in <module>
    from hivemind.averaging.averager import DecentralizedAverager
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/averaging/averager.py", line 20, in <module>
    from hivemind.averaging.allreduce import AllreduceException, AllReduceRunner, AveragingMode, GroupID
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/averaging/allreduce.py", line 7, in <module>
    from hivemind.averaging.partition import AllreduceException, BannedException, TensorPartContainer, TensorPartReducer
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/averaging/partition.py", line 11, in <module>
    from hivemind.compression import CompressionBase, CompressionInfo, NoCompression
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/compression/__init__.py", line 5, in <module>
    from hivemind.compression.adaptive import PerTensorCompression, RoleAdaptiveCompression, SizeAdaptiveCompression
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/compression/adaptive.py", line 6, in <module>
    from hivemind.compression.base import CompressionBase, CompressionInfo, Key, NoCompression, TensorRole
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/compression/base.py", line 12, in <module>
    from hivemind.utils.tensor_descr import TensorDescriptor
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/utils/__init__.py", line 1, in <module>
    from hivemind.utils.asyncio import *
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/utils/asyncio.py", line 169, in <module>
    class _AsyncContextWrapper(AbstractAsyncContextManager):
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/site-packages/hivemind/utils/asyncio.py", line 174, in _AsyncContextWrapper
    EXECUTOR_LOCK = mp.Lock()
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/multiprocessing/context.py", line 68, in Lock
    return Lock(ctx=self.get_context())
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/multiprocessing/synchronize.py", line 162, in __init__
    SemLock.__init__(self, SEMAPHORE, 1, 1, ctx=ctx)
  File "/home/xxx/anaconda3/envs/llm/lib/python3.9/multiprocessing/synchronize.py", line 57, in __init__
    sl = self._semlock = _multiprocessing.SemLock(
PermissionError: [Errno 13] Permission denied

To Reproduce
I try to use this library https://github.com/bigscience-workshop/petals.
According to the instructions of petals, I first created python3.9 environment,
then pip install -r requirements.txt, and when I tried to run it, this error occurred.

Environment
Please list:

• python version 3.9.17
• hivemind 1.1.9 pypi_0 pypi
• Please copy and paste the output from pytorch [environment collection script]

Collecting environment information...
PyTorch version: 2.0.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A

OS: Debian GNU/Linux 10 (buster) (x86_64)
GCC version: (Debian 8.3.0-6) 8.3.0
Clang version: Could not collect
CMake version: version 3.27.0
Libc version: glibc-2.28

Python version: 3.9.17 (main, Jul  5 2023, 20:41:20)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-4.19.0-23-amd64-x86_64-with-glibc2.28
Is CUDA available: True
CUDA runtime version: 11.7.64
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
GPU 4: NVIDIA GeForce RTX 3090
GPU 5: NVIDIA GeForce RTX 3090
GPU 6: NVIDIA GeForce RTX 3090
GPU 7: NVIDIA GeForce RTX 3090

Nvidia driver version: 515.76
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, 57 bits virtual
CPU(s):              112
On-line CPU(s) list: 0-111
Thread(s) per core:  2
Core(s) per socket:  28
Socket(s):           2
NUMA node(s):        2
Vendor ID:           GenuineIntel
CPU family:          6
Model:               106
Model name:          Intel(R) Xeon(R) Gold 6330 CPU @ 2.00GHz
Stepping:            6
CPU MHz:             2600.000
BogoMIPS:            4000.00
Virtualization:      VT-x
L1d cache:           48K
L1i cache:           32K
L2 cache:            1280K
L3 cache:            43008K
NUMA node0 CPU(s):   0-27,56-83
NUMA node1 CPU(s):   28-55,84-111
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 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 invpcid_single 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 rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp_epp avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities

Versions of relevant libraries:
[pip3] numpy==1.25.1
[pip3] torch==2.0.1
[pip3] triton==2.0.0
[conda] numpy                     1.25.1                   pypi_0    pypi
[conda] torch                     2.0.1                    pypi_0    pypi
[conda] triton                    2.0.0                    pypi_0    pypi