Nov 13, 2025
PolyBlocks is another interesting ML compiler, written using MLIR. It’s a startup incubated in IISc Bangalore, run by someone (Uday Bondhugula) who co-authored a paper on compiler optimizations for GPGPUs back in 2008 (17 years ago)!
Some of the compiler passes to keep in mind:
- fusion
- tiling
- use hardware acceleration (like tensor cores)
- constant folding
- perform redundant computation to avoid global memory accesses where profitable
- pack into buffers
- loop transformation
- unroll-and-jam (register tiling?)
- vectorization
- reorder execution for better spatial, temporary and group reuse
Scheduling approaches:
Nov 7, 2025
Tags: ml, compiler, onnx, ggml, sdkit, worklog
Wrote a simple script to convert ONNX to GGML. It auto-generates C++ code that calls the corresponding ggml functions (for each ONNX operator). This file can then be compiled and run like a normal C++ ggml program, and will produce the same results as the original model in PyTorch.
The generated file can work on multiple backends: CPU, CUDA, ROCm, Vulkan, Metal etc, by providing the correct compiler flags during cmake -B, e.g. -D GGML_CUDA=1 for CUDA.
Jan 13, 2025
Tags: easydiffusion, torchruntime, torch, ml
Spent the last few days writing torchruntime, which will automatically install the correct torch distribution based on the user’s OS and graphics card. This package was written by extracting this logic out of Easy Diffusion, and refactoring it into a cleaner implementation (with tests).
It can be installed (on Win/Linux/Mac) using pip install torchruntime.
The main intention is that it’ll be easier for developers to contribute updates (for e.g. for newer or older GPUs). It wasn’t easy to find or modify this code previously, since it was buried deep inside Easy Diffusion’s internals.