Torch compile: libcuda.so cannot found #107960
Comments
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I don't believe this is a When you're Also keep in mind that |
msaroufim
added
triaged
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Hey @msaroufim - many thanks for the detailed response! However, I'm not sure this originates from having multiple versions of PyTorch installed. If I uninstall the base version of PyTorch, and then install PyTorch nightly, I get the If you're confident this is a Colab issue, I can raise it with the Google team! But it would be great to rule out a PyTorch compile issue before doing this! |
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Trying your Colab notebook, I could verify that the issue isn't from PyTorch, but have found the workaround: After installing the nightly build, by running No The Verify inference both without compile and with compile:
The first call with compilation takes a long time (
We see a big speed increase even on the T4 GPU (although initial compilation does take quite a while). Keep in mind all the shell commands are to be run with a preceding |
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What is the problem and how can solve it? I don't understand. |
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@poly-mer 's solution worked for me, but there was no speedup for me, these are the commands I ran step by step: !export LC_ALL="en_US.UTF-8" |
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Same issue on the official pytorch cuda 2.1.0 docker image |
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I'm exausted. |
this works, thanks |
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I have the same problem, but also with the preinstalled torch version. import torch
torch.set_default_device("cuda:0")
@torch.compile
def test(x):
return torch.sin(x)
a = torch.zeros(100)
test(a)After a long backtrace it reports: Calling best wishes |
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I'm using Singularity on |
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Do you guys know how to solve "libcuda.so cannot found" if I don't have root access? Cuda drivers work with no problem (checked with nvidia-smi and torch). |
Downgrading |
Thanks, it works. |
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Closing, feel free to reopen if needed |
🐛 Describe the bug
Using
torch.compilewith a Colab T4 GPU fails and gives a very cryptic error running on nightly 2.1Error logs
Minified repro
https://colab.research.google.com/drive/1XwD2UpPoi6RFLHA9tcXL7BdbOgkKvQ_7?usp=sharing
Versions
PyTorch version: 2.1.0.dev20230825+cu121
Is debug build: False
CUDA used to build PyTorch: 12.1
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.2 LTS (x86_64)
GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version: 14.0.0-1ubuntu1.1
CMake version: version 3.27.2
Libc version: glibc-2.35
Python version: 3.10.12 (main, Jun 11 2023, 05:26:28) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.15.109+-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 525.105.17
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0
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: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 2
On-line CPU(s) list: 0,1
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) CPU @ 2.20GHz
CPU family: 6
Model: 79
Thread(s) per core: 2
Core(s) per socket: 1
Socket(s): 1
Stepping: 0
BogoMIPS: 4399.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 32 KiB (1 instance)
L1i cache: 32 KiB (1 instance)
L2 cache: 256 KiB (1 instance)
L3 cache: 55 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0,1
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable; SMT Host state unknown
Vulnerability Meltdown: Vulnerable
Vulnerability Mmio stale data: Vulnerable
Vulnerability Retbleed: Vulnerable
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, PBRSB-eIBRS: Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Vulnerable
Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] pytorch-triton==2.1.0+e6216047b8
[pip3] torch==2.1.0.dev20230825+cu121
[pip3] torchaudio==2.1.0.dev20230825+cu121
[pip3] torchdata==0.6.1
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.15.2
[pip3] torchvision==0.15.2+cu118
[pip3] triton==2.0.0
[conda] Could not collect
cc @ezyang @msaroufim @wconstab @bdhirsh @anijain2305
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