u/notforrob

This is Doom's actual renderer -- not a neural net imitating it. I wrote a compiler that turns computation graphs into transformer weights, then ported Doom's rendering algorithm into one.

No training anywhere: every weight was computed. The prompt carries the level and player position. When you feed the prompt into the model it generates drawing commands.

One frame is 53,747 generated tokens -- about 40 minutes on a B200. In 1993 Doom hit 35 fps on a 486. I hit 0.0004 fps on hardware a billion times more powerful.

Write-up: https://ood.dev/posts/doom/

Weights: https://huggingface.co/physicsrob/torchwright-doom-e1m1

I've been chasing the question of what algorithms a transformer can actually express -- separate from what it can learn. So I built a compiler: define a computation graph in ordinary Python, and it produces the weights of a transformer that executes the graph. The result is a standard Phi-3-architecture checkpoint that vanilla huggingface loads with no custom code and no trust_remote_code. Zero training in the pipeline.

Write-up (origin + how the constructions work): https://ood.dev/posts/torchwright-intro/

Repo (twelve runnable examples): https://github.com/physicsrob/torchwright

Hand-built transformer weights aren't a new idea. RASP defines a language whose primitives map onto transformer sublayers, and Tracr compiles RASP programs into actual weights. I wanted two things they don't aim for: expressing a computation graph in ordinary Python, and targeting a stock architecture, so the output loads in vanilla huggingface with no custom code.