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Published
20 hours ago •
pytorch
pytorch
ValueError: ('Unsupported kind: ', 'FRAGMENT')
I'm getting this import error when trying to import the libraty
from torchao.quantization import quantize_
File "/home/coder/.local/lib/python3.10/site-packages/torchao/__init__.py", line 31, in <module>
from torchao.quantization import (
File "/home/coder/.local/lib/python3.10/site-packages/torchao/quantization/__init__.py", line 7, in <module>
from .smoothquant import * # noqa: F403
File "/home/coder/.local/lib/python3.10/site-packages/torchao/quantization/smoothquant.py", line 18, in <module>
from .utils import (
File "/home/coder/.local/lib/python3.10/site-packages/torchao/quantization/utils.py", line 12, in <module>
from .quant_primitives import (
File "/home/coder/.local/lib/python3.10/site-packages/torchao/quantization/quant_primitives.py", line 78, in <module>
quant_lib = torch.library.Library("quant", "FRAGMENT")
File "/home/coder/.local/lib/python3.10/site-packages/torch/library.py", line 34, in __init__
raise ValueError("Unsupported kind: ", kind)
ValueError: ('Unsupported kind: ', 'FRAGMENT')
Cuda:
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.183.01 Driver Version: 535.183.01 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA L4 On | 00000000:35:00.0 Off | 0 |
| N/A 47C P0 20W / 72W | 0MiB / 23034MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
Env
PyTorch version: 2.0.1+cu117
Is debug build: False
CUDA used to build PyTorch: 11.7
ROCM used to build PyTorch: N/A
OneFlow version: none
Nexfort version: none
OneDiff version: none
OneDiffX version: none
OS: Ubuntu 20.04.6 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31
Python version: 3.10.14 (main, Apr 6 2024, 18:45:05) [GCC 9.4.0] (64-bit runtime)
Python platform: Linux-5.10.219-208.866.amzn2.x86_64-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: GPU 0: NVIDIA L4
Nvidia driver version: 535.183.01
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.2.4
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.2.4
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: 48 bits physical, 48 bits virtual
CPU(s): 16
On-line CPU(s) list: 0-15
Thread(s) per core: 2
Core(s) per socket: 8
Socket(s): 1
NUMA node(s): 1
Vendor ID: AuthenticAMD
CPU family: 25
Model: 1
Model name: AMD EPYC 7R13 Processor
Stepping: 1
CPU MHz: 2944.343
BogoMIPS: 5299.99
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 256 KiB
L1i cache: 256 KiB
L2 cache: 4 MiB
L3 cache: 32 MiB
NUMA node0 CPU(s): 0-15
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 Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET, no microcode
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 tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Versions of relevant libraries:
[pip3] diffusers==0.30.0
[pip3] numpy==1.24.0
[pip3] open-clip-torch==2.20.0
[pip3] pytorch-lightning==2.0.1
[pip3] torch==2.0.1
[pip3] torchao==0.4.0
[pip3] torchmetrics==1.4.2
[pip3] torchsde==0.2.6
[pip3] torchvision==0.15.2
[pip3] transformers==4.44.2
[pip3] triton==2.0.0
[conda] Could not collect
This issue did not happen when using Nvidia A10g / 24 GB.
