RTX5090d:ImportError: cannot import name 'EPOCH_OUTPUT' from 'pytorch_lightning.utilities.types'

[paomian001]

Description & Motivation

RTX5090d can only install the nightly versions of CUDA12.8 and torch2.8. Is there a corresponding PyTorch_lightning version for CUDA12.8 and torch2.8 that can be installed？

Pitch

Alternatives

No response

Additional context

Versions

PyTorch version: 2.8.0.dev20250416+cu128 Is debug build: False CUDA used to build PyTorch: 12.8 ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.5 LTS (x86_64) GCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 Clang version: Could not collect CMake version: Could not collect Libc version: glibc-2.35

Python version: 3.9.21 (main, Dec 11 2024, 16:24:11) [GCC 11.2.0] (64-bit runtime) Python platform: Linux-6.8.0-57-generic-x86_64-with-glibc2.35 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA GeForce RTX 5090 D Nvidia driver version: 570.133.07 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True

CPU: 架构： x86_64 CPU 运行模式： 32-bit, 64-bit Address sizes: 39 bits physical, 48 bits virtual 字节序： Little Endian CPU: 32 在线 CPU 列表： 0-31 厂商 ID： GenuineIntel 型号名称： Intel(R) Core(TM) i9-14900KF CPU 系列： 6 型号： 183 每个核的线程数： 2 每个座的核数： 24 座： 1 步进： 1 CPU 最大 MHz： 6000.0000 CPU 最小 MHz： 800.0000 BogoMIPS： 6374.40 标记： 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_tart arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx est tm2 ssse3 sdbg fma cx16 xtpr pdcm sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb intel_pt sha_ni xsaveopt xsavec xgetbv1 xsaves split_lock_detect user_shstk avx_vnni dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi vnmi umip pku ospke waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize arch_lbr ibt flush_l1d arch_capabilities 虚拟化： VT-x L1d 缓存： 896 KiB (24 instances) L1i 缓存： 1.3 MiB (24 instances) L2 缓存： 32 MiB (12 instances) L3 缓存： 36 MiB (1 instance) NUMA 节点： 1 NUMA 节点0 CPU： 0-31 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: Mitigation; Clear Register File Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S Vulnerability Srbds: Not affected Vulnerability Tsx async abort: Not affected

Versions of relevant libraries: [pip3] numpy==1.26.4 [pip3] nvidia-cublas-cu12==12.8.3.14 [pip3] nvidia-cuda-cupti-cu12==12.8.57 [pip3] nvidia-cuda-nvrtc-cu12==12.8.61 [pip3] nvidia-cuda-runtime-cu12==12.8.57 [pip3] nvidia-cudnn-cu12==9.8.0.87 [pip3] nvidia-cufft-cu12==11.3.3.41 [pip3] nvidia-curand-cu12==10.3.9.55 [pip3] nvidia-cusolver-cu12==11.7.2.55 [pip3] nvidia-cusparse-cu12==12.5.7.53 [pip3] nvidia-cusparselt-cu12==0.6.3 [pip3] nvidia-nccl-cu12==2.26.2 [pip3] nvidia-nvjitlink-cu12==12.8.61 [pip3] nvidia-nvtx-cu12==12.8.55 [pip3] pytorch-lightning==2.5.1 [pip3] pytorch-msssim==1.0.0 [pip3] pytorch-triton==3.3.0+git96316ce5 [pip3] torch==2.8.0.dev20250416+cu128 [pip3] torch-geometric==2.6.1 [pip3] torchaudio==2.6.0.dev20250416+cu128 [pip3] torchmetrics==1.7.1 [pip3] torchvision==0.22.0.dev20250416+cu128 [conda] numpy 1.26.4 pypi_0 pypi [conda] nvidia-cublas-cu12 12.8.3.14 pypi_0 pypi [conda] nvidia-cuda-cupti-cu12 12.8.57 pypi_0 pypi [conda] nvidia-cuda-nvrtc-cu12 12.8.61 pypi_0 pypi [conda] nvidia-cuda-runtime-cu12 12.8.57 pypi_0 pypi [conda] nvidia-cudnn-cu12 9.8.0.87 pypi_0 pypi [conda] nvidia-cufft-cu12 11.3.3.41 pypi_0 pypi [conda] nvidia-curand-cu12 10.3.9.55 pypi_0 pypi [conda] nvidia-cusolver-cu12 11.7.2.55 pypi_0 pypi [conda] nvidia-cusparse-cu12 12.5.7.53 pypi_0 pypi [conda] nvidia-cusparselt-cu12 0.6.3 pypi_0 pypi [conda] nvidia-nccl-cu12 2.26.2 pypi_0 pypi [conda] nvidia-nvjitlink-cu12 12.8.61 pypi_0 pypi [conda] nvidia-nvtx-cu12 12.8.55 pypi_0 pypi [conda] pytorch-lightning 2.5.1 pypi_0 pypi [conda] pytorch-msssim 1.0.0 pypi_0 pypi [conda] pytorch-triton 3.3.0+git96316ce5 pypi_0 pypi [conda] torch 2.8.0.dev20250416+cu128 pypi_0 pypi [conda] torch-geometric 2.6.1 pypi_0 pypi [conda] torchaudio 2.6.0.dev20250416+cu128 pypi_0 pypi [conda] torchmetrics 1.7.1 pypi_0 pypi [conda] torchvision 0.22.0.dev20250416+cu128 pypi_0 pypi

cc @lantiga @borda

[Zyriix]

Upvoting this We really need support for torch 2.8 and cuda 12.8

[paomian001]

UpUpUp

[Borda]

hi, could you pls elaborate why or when in past this import worked?

[paomian001]

hi, could you pls elaborate why or when in past this import worked?

Maybe the POCH_OUTPUT variable was included in the previous types file？

[paomian001]

hi, could you pls elaborate why or when in past this import worked?

pytorch_lighting is still broken for Torch 2.7 and CUDA 12.8 - we need it for RTX 5000 series. @Borda

[paomian001]

Upvoting this We really need support for torch 2.8 and cuda 12.8

May I ask if you have resolved the issue of pytorch_lightning adapting to 50 series GTX?

[lovelytotoro1]

Hi, I think the issue might be with your code rather than the PyTorch Lightning framework itself.

The reason is that starting from PyTorch Lightning 2.0.0, the EPOCH_OUTPUT argument was removed. I encountered a similar problem recently. In fact, this is not about PyTorch Lightning not supporting the 50-series GPUs; it’s that your current code is incompatible with the newer version of PyTorch Lightning.

[SkafteNicki]

From the stacktrace I can see that it seems to be that you are executing code from this repository: https://github.com/huai-chang/DiffEIC which tries to import EPOCH_OUTPUT here https://github.com/huai-chang/DiffEIC/blob/c689ca7f88f8d4a57d82981afa3657482f73dc35/model/diffeic.py#L13 this variable has not been in the codebase for a long time, but it was present in v1.5.0 which they also specify in their requirement file https://github.com/huai-chang/DiffEIC/blob/c689ca7f88f8d4a57d82981afa3657482f73dc35/requirements.txt#L14 Please use that version if you want to run that codebase. Closing issue.