Builder ffmpegsos-solaris10-sparc Build #13726

Results:

Failed shell_2 shell_3 shell_4 shell_5

SourceStamp:

BuildSlave:

Reason:

The SingleBranchScheduler scheduler named 'schedule-ffmpegsos-solaris10-sparc' triggered this build

Steps and Logfiles:

1. git update ( 7 mins, 10 secs )
   1. stdio
2. shell 'gsed -i ...' ( 3 secs )
   1. stdio
3. shell_1 'gsed -i ...' ( 1 secs )
   1. stdio
4. shell_2 'gsed -i ...' failed ( 0 secs )
   1. stdio
5. shell_3 './configure --samples="../../../ffmpeg/fate-suite" ...' failed ( 23 secs )
   1. stdio
   2. config.log
6. shell_4 'gmake fate-rsync' failed ( 0 secs )
   1. stdio
7. shell_5 '../../../ffmpeg/fate.sh ../../../ffmpeg/fate_config_sos.sh' failed ( 1 secs )
   1. stdio
   2. configure.log
   3. compile.log
   4. test.log

Build Properties:

Forced Build Properties:

Responsible Users:

1. Raja-89

   imraja729ohnoyoudont@gmail.com

Timing:

All Changes:

:

1. Change #276439

   Category	ffmpeg
   Changed by	Raja-89 <imraja729ohnoyoudont@gmail.com>
   Changed at	Wed 29 Jul 2026 14:37:03
   Repository	https://git.ffmpeg.org/ffmpeg.git
   Project	ffmpeg
   Branch	master
   Revision	a234fc1130e6b17d550371dede849918202a303a

   Comments

   avfilter/dnn: prevent crash on parameterless LibTorch models
   When loading a TorchScript model that does not contain any learnable
   parameters (e.g., a purely functional model), the Torch backend would
   crash during inference. This occurred because the code attempted to
   dereference the first iterator of the model's parameter list
   `parameters().begin()` to determine the device, which results in
   Undefined Behavior when the parameter list is empty.
   This commit fixes the issue by determining the inference device directly
   from the user-configured `ctx->device` string instead of probing the
   model parameters, allowing parameterless models to execute safely.
   
   Testing:
   1. Generate a parameterless model:
   cat << 'EOF' > generate_model.py
   import torch
   class DummyModel(torch.nn.Module):
       def forward(self, x):
           return x
   scripted_model = torch.jit.script(DummyModel())
   scripted_model.save("dummy_model.pt")
   EOF
   python3 generate_model.py
   2. Run inference (previously crashed, now succeeds):
   ./ffmpeg -y -i input.mp4 -vf 'format=rgb24,dnn_processing=dnn_backend=torch:model=dummy_model.pt' -frames:v 5 -f null -
   Signed-off-by: Raja Rathour <imraja729@gmail.com>

   Changed files

   • libavfilter/dnn/dnn_backend_torch.cpp