spork-metal 0.2.0

A tracing Python DSL for writing Metal compute kernels for Apple GPUs.

Spork: A Tracing DSL for Apple GPUs

Spork is a DSL for writing kernels for Apple GPUs. You can think of it as a Python wrapper on top of Metal, Apple's GPU language. This makes development more convenient, since it makes the kernels live in the same source language as most ML code, and makes it easy to verify correctness against NumPy.

Unlike Triton, Spork is a tracing DSL rather than a parsing-based DSL. This means that a Spork program is fundamentally a Python program that creates a Metal program. Spork can use any Python libraries to aid in metaprogramming.

Installation

Spork runs on Apple Silicon (or any Mac with Metal). Install it from PyPI:

pip install spork-metal

Or with uv:

uv add spork-metal

The package is published as spork-metal on PyPI; the import name is spork:

import spork as sk

Example: Matrix Addition

One of the most basic kernels is a matrix-addition kernel. In Numpy, you could write

shape = (1024, 1024)
A = np.random.randn(*shape).astype(np.float32)
B = np.random.randn(*shape).astype(np.float32)
out = A + B

A Metal kernel to perform this operation would look like this:

#include <metal_stdlib>
using namespace metal;

kernel void matrix_add(
    device float *out [[buffer(0)]],
    device const float *A [[buffer(1)]],
    device const float *B [[buffer(2)]],
    uint index [[thread_position_in_grid]])
{
    out[index] = A[index] + B[index];
}

Notice that it exists in a separate source file and requires special code to compile, link to, and invoke. An equivalent Spork kernel looks like this:

@sk.jit
def matrix_add(
    out   : sk.DevicePointer[sk.dt.float32],
    A     : sk.DevicePointer[sk.dt.float32],
    B     : sk.DevicePointer[sk.dt.float32],
    index : sk.Uint[sk.ThreadPositionInGrid],
):
    out[index] = A[index] + B[index]

Notice that we have a direct correspondance here between the Spork kernel and the Metal kernel. We declare the types of our inputs, and we can take the attribute parameters that we take in Metal.

Then to actually invoke and run kernel, we simply call it from Python with Numpy arrays, and like magic it runs your kernel!

matrix_add[
    (int(np.prod(shape)) // 128, 1, 1),
    (128, 1, 1),
](
    C,
    A,
    B,
)

Notice that in the brackets, we provide two parameters. The first is the Grid size, and the second is the Warpgroup size. In the parentheses, we supply the Numpy tensors we wish to use. Notice that here we're taking a pointer to C, which is allocated via Numpy but written to from the Spork kernel.