Active project
TensionForge
A verifier-first OpenCL training runtime for legacy AMD hardware, built around exact parity receipts before performance claims.
What problem it addresses
Legacy commodity GPUs are poorly served by current ML stacks, while custom kernels can easily appear correct without proving numerical parity.
How it works
Implement persistent device tensors, fused linear/tension operations, backward passes, AdamW, and recurrent cells, then compare each stage against a reference implementation.
What exists now
Correct enough to investigate honestly; too slow to present as practical acceleration.
Evidence / receipts
The runtime demonstrates OpenCL SAXPY, tiled matmul, full linear training, and Ten-SON forward/backward/multi-step optimizer parity.
Limits
It is currently much slower than PyTorch CPU on the tested workloads. It is not a performance win yet.
Next milestone
Reach within 2× of the CPU reference or explicitly classify the project as a verification/research backend rather than an accelerator.
How to run / inspect
Install / open
git clone https://github.com/BoggersTheFish/TensionForge && cd TensionForgeInspect
Run the parity harnesses, then profile launch count, dispatch overhead, small-matrix occupancy, serial recurrence, temporary buffers, and device readbacks.
Links
Focus tags
Related receipts