Multiprocessing leads to decoding error

🐛 Describe the bug

I am trying to assemble batches of images of random frames from multiple videos.
This is my code:

import torch
from torchcodec.decoders import VideoDecoder
from tqdm import tqdm

class Dataset(torch.utils.data.Dataset):

    def __init__(self,video_files:list[str]):
        self.videos = [VideoDecoder(f,device="cpu",num_ffmpeg_threads=1) for f in video_files]
        self.num_frames = len(self.videos[0])

    def __len__(self):
        return self.num_frames*len(self.videos)

    def __getitem__(self, item: int) -> torch.Tensor:
        
        frame_id = item%self.num_frames
        cam_id = item//self.num_frames
        return self.videos[cam_id].get_frame_at(frame_id).data
        
    
ds = Dataset(["video_1.mp4","video_2.mp4","video_3.mp4"])

trainloader = torch.utils.data.DataLoader(
    ds,
    batch_size=4,
    num_workers=1,
)
loader = iter(trainloader)
for l in tqdm(loader):
    pass

As long as num_workers is set to zero or one everything works.
Once I increase the num_workers i get this error for random batches:

RuntimeError: Could not push packet to decoder: Invalid data found when processing input

This seams to be a FFMEG error that only occurs when multiprocessing (by the Dataloader) is used.

Versions

Collecting environment information...
PyTorch version: 2.7.1+cu128
Is debug build: False
CUDA used to build PyTorch: 12.8
ROCM used to build PyTorch: N/A

OS: Ubuntu 24.04.2 LTS (x86_64)
GCC version: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version: Could not collect
CMake version: version 3.28.3
Libc version: glibc-2.39

Python version: 3.13.5 | packaged by Anaconda, Inc. | (main, Jun 12 2025, 16:09:02) [GCC 11.2.0] (64-bit runtime)
Python platform: Linux-6.11.0-29-generic-x86_64-with-glibc2.39
Is CUDA available: True
CUDA runtime version: 12.8.93
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 5090
Nvidia driver version: 570.133.20
cuDNN version: Could not collect
Is XPU available: False
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: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 32
On-line CPU(s) list: 0-31
Vendor ID: AuthenticAMD
Model name: AMD Ryzen 9 9950X 16-Core Processor
CPU family: 26
Model: 68
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 1
Stepping: 0
Frequency boost: enabled
CPU(s) scaling MHz: 84%
CPU max MHz: 5752.0000
CPU min MHz: 600.0000
BogoMIPS: 8583.65
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 amd_lbr_v2 nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 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 tsc_adjust 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 avx_vnni avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid bus_lock_detect movdiri movdir64b overflow_recov succor smca fsrm avx512_vp2intersect flush_l1d amd_lbr_pmc_freeze
Virtualization: AMD-V
L1d cache: 768 KiB (16 instances)
L1i cache: 512 KiB (16 instances)
L2 cache: 16 MiB (16 instances)
L3 cache: 64 MiB (2 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 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: Not affected
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; 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] 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==9.7.1.26
[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] torch==2.7.1+cu128
[pip3] torch-fidelity==0.3.0
[pip3] torch-tb-profiler==0.4.3
[pip3] torchaudio==2.7.1+cu128
[pip3] torchcodec==0.4.0
[pip3] torchmetrics==1.7.3
[pip3] torchvision==0.22.1+cu128
[pip3] triton==3.3.1
[conda] cuda-cudart 12.8.90 0 nvidia/label/cuda-12.8.1
[conda] cuda-cudart-dev 12.8.90 0 nvidia/label/cuda-12.8.1
[conda] cuda-cudart-dev_linux-64 12.8.90 0 nvidia/label/cuda-12.8.1
[conda] cuda-cudart-static 12.8.90 0 nvidia/label/cuda-12.8.1
[conda] cuda-cudart-static_linux-64 12.8.90 0 nvidia/label/cuda-12.8.1
[conda] cuda-cudart_linux-64 12.8.90 0 nvidia/label/cuda-12.8.1
[conda] cuda-cupti 12.8.90 0 nvidia
[conda] cuda-cupti-dev 12.8.90 0 nvidia
[conda] cuda-libraries 12.8.1 0 nvidia
[conda] cuda-libraries-dev 12.8.1 0 nvidia
[conda] cuda-nvrtc 12.8.93 0 nvidia
[conda] cuda-nvrtc-dev 12.8.93 0 nvidia
[conda] cuda-nvtx 12.8.90 0 nvidia
[conda] cuda-opencl 12.8.90 0 nvidia
[conda] cuda-opencl-dev 12.8.90 0 nvidia
[conda] cuda-runtime 12.8.1 0 nvidia
[conda] libcublas 12.8.4.1 0 nvidia
[conda] libcublas-dev 12.8.4.1 0 nvidia
[conda] libcufft 11.3.3.83 0 nvidia
[conda] libcufft-dev 11.3.3.83 0 nvidia
[conda] libcurand 10.3.9.90 0 nvidia
[conda] libcurand-dev 10.3.9.90 0 nvidia
[conda] libcusolver 11.7.3.90 0 nvidia
[conda] libcusolver-dev 11.7.3.90 0 nvidia
[conda] libcusparse 12.5.8.93 0 nvidia
[conda] libcusparse-dev 12.5.8.93 0 nvidia
[conda] libnvjitlink 12.8.93 1 nvidia
[conda] libnvjitlink-dev 12.8.93 1 nvidia
[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 9.7.1.26 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] torch 2.7.1+cu128 pypi_0 pypi
[conda] torch-fidelity 0.3.0 pypi_0 pypi
[conda] torch-tb-profiler 0.4.3 pypi_0 pypi
[conda] torchaudio 2.7.1+cu128 pypi_0 pypi
[conda] torchcodec 0.4.0 pypi_0 pypi
[conda] torchmetrics 1.7.3 pypi_0 pypi
[conda] torchvision 0.22.1+cu128 pypi_0 pypi
[conda] triton 3.3.1 pypi_0 pypi

Start by reproducing the provided Dataset and DataLoader example using VideoDecoder with num_workers set above one, then compare it with zero and one worker. Trace how VideoDecoder instances are created and used across DataLoader workers. Done means random batches decode reliably with multiple workers without the FFmpeg invalid-data error.