forge-metal 0.0.1

Forge crafts Metal: an Array framework with eager execution and JIT graph compilation for Apple Silicon GPUs

Forge

Forge crafts Metal: an Array framework with eager execution and JIT graph compilation for Apple Silicon GPUs

Forge was initially intended as a Python library to run Metal Kernels on Apple Silicon (M-series) GPUs. Through it's development, it's picked up more features of general array/tensor libraries like numpy, pytorch, and mlx.

Then why build Forge?

I built Forge and continue to work on it primarily because it's a passion project with a lot to learn from. In writing the code for this project, I learned about software design, compilers, GPU kernels, Python libraries, and testing. I hope to learn much more as I continue working on the project. I also believe that in due time, certain features could be done better than in existing available frameworks.

Features:

1. Eager Execution: operations run asynchronously, seamlessly on GPU with a familiar API design
2. JIT Graph Compilation (WIP): functions are compiled and optimized using the @forge decorator to be re-run more efficiently

Example:

This simple code snippet below will add the two arrays using your GPU.

from Forge import Array
a1 = Array([[1.0, 2.0], [3.0, 4.0]])
a2 = Array([[4.0, 5.0], [6.0, 7.0]])
result = a1 + a2

Further information

If this sounds interesting and you'd like to try out the library, you can install it and run it on your own macbook using this guide.

If you find anything interesting around and wish to contribute feel free to. You can take a look at this guide to get setup and contribute or shoot me a message @kellen05 on discord.

Thanks be to our contributors!

• @BillJJ
• @DawDa07