nvidia RTX 5090 + pytorch=2.8 + torchcodec=0.3, An error occurred: NotImplementedError

[HUPg-95]

🐛 Describe the bug

import torch
import torchcodec
from torchcodec.decoders import VideoDecoder

# A succinct reproducing example trimmed down to the essential parts:
decoder = VideoDecoder("./111.mp4")  # Help! This fails!

Since my graphics card is 5090, I can only use PyTorch=2.8 nightly, but the Torch codec is not compatible with PyTorch=2.8. How can I solve this problem,? Here is the error message：

Traceback (most recent call last):
File "/data/lerobot/unitree_IL_lerobot/test.py", line 6, in
decoder = VideoDecoder("./111.mp4") # Help! This fails!
File "/root/miniforge3/envs/lerobot/lib/python3.10/site-packages/torchcodec/decoders/_video_decoder.py", line 89, in init
self._decoder = create_decoder(source=source, seek_mode=seek_mode)
File "/root/miniforge3/envs/lerobot/lib/python3.10/site-packages/torchcodec/decoders/_decoder_utils.py", line 27, in create_decoder
return core.create_from_file(source, seek_mode)
File "/root/miniforge3/envs/lerobot/lib/python3.10/site-packages/torch/_ops.py", line 806, in call
return self._op(*args, **kwargs)
NotImplementedError: There were no tensor arguments to this function (e.g., you passed an empty list of Tensors), but no fallback function is registered for schema torchcodec_ns::create_from_file. This usually means that this function requires a non-empty list of Tensors, or that you (the operator writer) forgot to register a fallback function. Available functions are [Meta, BackendSelect, Python, FuncTorchDynamicLayerBackMode, Functionalize, Named, Conjugate, Negative, ZeroTensor, ADInplaceOrView, AutogradOther, AutogradCPU, AutogradCUDA, AutogradXLA, AutogradMPS, AutogradXPU, AutogradHPU, AutogradLazy, AutogradMTIA, AutogradMAIA, AutogradMeta, Tracer, AutocastCPU, AutocastMTIA, AutocastMAIA, AutocastXPU, AutocastMPS, AutocastCUDA, FuncTorchBatched, BatchedNestedTensor, FuncTorchVmapMode, Batched, VmapMode, FuncTorchGradWrapper, PythonTLSSnapshot, FuncTorchDynamicLayerFrontMode, PreDispatch, PythonDispatcher].

Meta: registered at /dev/null:160 [kernel]
BackendSelect: fallthrough registered at /pytorch/aten/src/ATen/core/BackendSelectFallbackKernel.cpp:3 [backend fallback]
Python: registered at /__w/torchcodec/torchcodec/pytorch/torchcodec/src/torchcodec/_core/custom_ops.cpp:648 [kernel]
FuncTorchDynamicLayerBackMode: registered at /pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:479 [backend fallback]
Functionalize: registered at /pytorch/aten/src/ATen/FunctionalizeFallbackKernel.cpp:349 [backend fallback]
Named: registered at /pytorch/aten/src/ATen/core/NamedRegistrations.cpp:7 [backend fallback]
Conjugate: registered at /pytorch/aten/src/ATen/ConjugateFallback.cpp:17 [backend fallback]
Negative: registered at /pytorch/aten/src/ATen/native/NegateFallback.cpp:18 [backend fallback]
ZeroTensor: registered at /pytorch/aten/src/ATen/ZeroTensorFallback.cpp:86 [backend fallback]
ADInplaceOrView: fallthrough registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:104 [backend fallback]
AutogradOther: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:63 [backend fallback]
AutogradCPU: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:67 [backend fallback]
AutogradCUDA: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:75 [backend fallback]
AutogradXLA: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:87 [backend fallback]
AutogradMPS: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:95 [backend fallback]
AutogradXPU: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:71 [backend fallback]
AutogradHPU: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:108 [backend fallback]
AutogradLazy: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:91 [backend fallback]
AutogradMTIA: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:79 [backend fallback]
AutogradMAIA: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:83 [backend fallback]
AutogradMeta: registered at /pytorch/aten/src/ATen/core/VariableFallbackKernel.cpp:99 [backend fallback]
Tracer: registered at /pytorch/torch/csrc/autograd/TraceTypeManual.cpp:294 [backend fallback]
AutocastCPU: fallthrough registered at /pytorch/aten/src/ATen/autocast_mode.cpp:322 [backend fallback]
AutocastMTIA: fallthrough registered at /pytorch/aten/src/ATen/autocast_mode.cpp:466 [backend fallback]
AutocastMAIA: fallthrough registered at /pytorch/aten/src/ATen/autocast_mode.cpp:504 [backend fallback]
AutocastXPU: fallthrough registered at /pytorch/aten/src/ATen/autocast_mode.cpp:542 [backend fallback]
AutocastMPS: fallthrough registered at /pytorch/aten/src/ATen/autocast_mode.cpp:209 [backend fallback]
AutocastCUDA: fallthrough registered at /pytorch/aten/src/ATen/autocast_mode.cpp:165 [backend fallback]
FuncTorchBatched: registered at /pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:731 [backend fallback]
BatchedNestedTensor: registered at /pytorch/aten/src/ATen/functorch/LegacyBatchingRegistrations.cpp:758 [backend fallback]
FuncTorchVmapMode: fallthrough registered at /pytorch/aten/src/ATen/functorch/VmapModeRegistrations.cpp:27 [backend fallback]
Batched: registered at /pytorch/aten/src/ATen/LegacyBatchingRegistrations.cpp:1075 [backend fallback]
VmapMode: fallthrough registered at /pytorch/aten/src/ATen/VmapModeRegistrations.cpp:33 [backend fallback]
FuncTorchGradWrapper: registered at /pytorch/aten/src/ATen/functorch/TensorWrapper.cpp:208 [backend fallback]
PythonTLSSnapshot: registered at /pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:202 [backend fallback]
FuncTorchDynamicLayerFrontMode: registered at /pytorch/aten/src/ATen/functorch/DynamicLayer.cpp:475 [backend fallback]
PreDispatch: registered at /pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:206 [backend fallback]
PythonDispatcher: registered at /pytorch/aten/src/ATen/core/PythonFallbackKernel.cpp:198 [backend fallback]

Versions

PyTorch version: 2.8.0.dev20250506+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: version 4.0.0
Libc version: glibc-2.35

Python version: 3.10.13 | packaged by conda-forge | (main, Dec 23 2023, 15:36:39) [GCC 12.3.0] (64-bit runtime)
Python platform: Linux-6.8.0-58-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.8.61
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 5090
Nvidia driver version: 570.86.16
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: 52 bits physical, 57 bits virtual
字节序： Little Endian
CPU: 48
在线 CPU 列表： 0-47
厂商 ID： AuthenticAMD
型号名称： AMD Ryzen Threadripper PRO 7965WX 24-Cores
CPU 系列： 25
型号： 24
每个核的线程数： 2
每个座的核数： 24
座： 1
步进： 1
CPU 最大 MHz： 5362.0000
CPU 最小 MHz： 545.0000
BogoMIPS： 8386.67
标记： 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 amd_lbr_v2 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 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc amd_ibpb_ret arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d debug_swap
虚拟化： AMD-V
L1d 缓存： 768 KiB (24 instances)
L1i 缓存： 768 KiB (24 instances)
L2 缓存： 24 MiB (24 instances)
L3 缓存： 128 MiB (4 instances)
NUMA 节点： 1
NUMA 节点0 CPU： 0-47
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
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; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

Versions of relevant libraries:
[pip3] ament-flake8==0.12.12
[pip3] mypy_extensions==1.1.0
[pip3] numpy==2.2.5
[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-triton==3.3.0+git96316ce5
[pip3] torch==2.8.0.dev20250506+cu128
[pip3] torchaudio==2.6.0.dev20250506+cu128
[pip3] torchcodec==0.3.0+cu128
[pip3] torchvision==0.22.0.dev20250506+cu128
[pip3] triton==3.2.0
[conda] libopenvino-pytorch-frontend 2025.0.0 h5888daf_3 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
[conda] numpy 2.2.5 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-triton 3.3.0+git96316ce5 pypi_0 pypi
[conda] torch 2.8.0.dev20250506+cu128 pypi_0 pypi
[conda] torchaudio 2.6.0.dev20250506+cu128 pypi_0 pypi
[conda] torchcodec 0.3.0+cu128 pypi_0 pypi
[conda] torchvision 0.22.0.dev20250506+cu128 pypi_0 pypi
[conda] triton 3.2.0 pypi_0 pypi

[NicolasHug]

[conda] torch 2.8.0.dev20250506+cu128 pypi_0 pypi
[conda] torchcodec 0.3.0+cu128 pypi_0 pypi

TorchCodec 0.3 works only against torch 2.7.

Since you're using the 2.8 nightly, you should be able to make torchcodec work by installing the nightly torchcodec. On cu128 I guess this should work:

pip3 install --pre torch torchcodec --index-url https://download.pytorch.org/whl/nightly/cu128

Let us know if there are further issues, I'll close this in the mean time.