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Pytorch import is extremely slow #1232

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@Delaunay

Description

@Delaunay

🐛 Describe the bug

$ time python -c "import torch.cuda; print(torch.cuda.is_available())"
True

real    3m36.487s
user    0m4.613s
sys     0m34.452s

Versions

Collecting environment information...

PyTorch version: 2.0.0+rocm5.4.2
Is debug build: False
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: 5.4.22803-474e8620

OS: Ubuntu 20.04 LTS (x86_64)
GCC version: (GCC) 7.4.0
Clang version: Could not collect
CMake version: version 3.26.1
Libc version: glibc-2.31

Python version: 3.9.16 (main, Jan 11 2023, 16:05:54)  [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-5.15.0-71-generic-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: AMD Instinct MI250X/MI250
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: 5.4.22803
MIOpen runtime version: 2.19.0
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Byte Order:                      Little Endian
Address sizes:                   48 bits physical, 48 bits virtual
CPU(s):                          256
On-line CPU(s) list:             0-255
Thread(s) per core:              2
Core(s) per socket:              64
Socket(s):                       2
NUMA node(s):                    2
Vendor ID:                       AuthenticAMD
CPU family:                      25
Model:                           1
Model name:                      AMD EPYC 7713 64-Core Processor
Stepping:                        1
Frequency boost:                 enabled
CPU MHz:                         2553.233
CPU max MHz:                     3720.7029
CPU min MHz:                     1500.0000
BogoMIPS:                        3992.55
Virtualization:                  AMD-V
L1d cache:                       4 MiB
L1i cache:                       4 MiB
L2 cache:                        64 MiB
L3 cache:                        512 MiB
NUMA node0 CPU(s):               0-63,128-191
NUMA node1 CPU(s):               64-127,192-255
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: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Retpolines, IBPB conditional, IBRS_FW, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected
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 pcid sse4_1 sse4_2 x2apic 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 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves 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 v_vmsave_vmload vgif v_spec_ctrl umip pku ospke vaes vpclmulqdq rdpid overflow_recov succor smca fsrm

Versions of relevant libraries:
[pip3] mypy-extensions==1.0.0
[pip3] numpy==1.24.2
[pip3] pytorch-triton-rocm==2.0.1
[pip3] torch==2.0.0+rocm5.4.2
[pip3] torchaudio==2.0.1+rocm5.4.2
[pip3] torchmetrics==0.11.4
[pip3] torchrec==0.4.0
[pip3] torchvision==0.15.1+rocm5.4.2
[pip3] torchviz==0.0.2
[pip3] torchx==0.5.0
[conda] numpy                     1.24.2                   pypi_0    pypi
[conda] torch                     2.0.0+cu118              pypi_0    pypi
[conda] torchaudio                2.0.1+cu118              pypi_0    pypi
[conda] torchmetrics              0.11.4                   pypi_0    pypi
[conda] torchrec                  0.4.0                    pypi_0    pypi
[conda] torchvision               0.15.1+cu118             pypi_0    pypi
[conda] torchviz                  0.0.2                    pypi_0    pypi
[conda] torchx                    0.5.0                    pypi_0    pypi
[conda] triton                    2.0.0                    pypi_0    pypi

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