search

pytorch / ao

PyTorch native quantization and sparsity for training and inference
BSD 3-Clause "New" or "Revised" License
749 stars 95 forks source link

ValueError: ('Unsupported kind: ', 'FRAGMENT') #900

Open loretoparisi opened 2 days ago

loretoparisi commented 2 days ago

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.

jerryzh168 commented 2 days ago

we only support pytorch 2.2+ right now and probably will be dropping 2.2. can you upgrade your PyTorch?