GitHub - corsix/amx: Apple AMX Instruction Set

Contemporary M1 / M2 machines from Apple have (at least) four different ways for low-level programmers to perform heavy computations:

1. Standard ARMv8 SIMD/NEON vector instructions on CPU cores (128 bits wide, issue up to four per cycle on Firestorm)
2. Apple's undocumented AMX instructions, issued from CPU, executed on a special accelerator execution unit
3. The Neural Engine (called ANE or NPU)
4. The GPU (e.g. Metal Compute Shaders)

This repository is all about the 2nd of those: Apple's AMX instructions. Note that these instructions are neither documented nor supported by Apple. As a source of potential great confusion, Apple's AMX instructions are completely distinct from Intel's AMX instructions, though both are intended for issuing matrix multiply operations from a CPU.

The research was done on an Apple M1 Max (2021). Older or newer chips might have different AMX instructions. Some sources report that the M1 contains version 2 of the AMX instructions, which seems plausible (possibly everything using 7-bit writemasks comes from version 1, and everything using 9-bit writemasks is new in version 2).

A good one-image summary of AMX is the following figure from abandoned patent US20180074824A1. Consider a 32x32 grid of compute units, where each unit can perform 16-bit multiply-accumulate, or a 2x2 subgrid of units can perform 32-bit multiply-accumulate, or a 4x4 subgrid can perform 64-bit multiply-accumulate. To feed this grid, there is a pool of X registers each containing 32 16-bit elements (or 16 32-bit elements, or 8 64-bit elements) and a pool of Y registers similarly containing 32 16-bit elements (or 16 32-bit elements, or 8 64-bit elements). A single instruction can perform a full outer product: multiply every element of an X register with every element of a Y register, and accumulate with the Z element in the corresponding position.

A single row of the 32x32 grid can also be used to perform vector operations (rather than matrix operations) between X and YT.

In terms of available data types, the general pattern is:

• IEEE754 f16 or f32 or f64 (same width for all three fused-multiply-add operands)
• IEEE754 f16 multiplicands, accumulating onto f32
• Integer 8-bit or 16-bit multiplicands, accumulating onto 16 or 32 bits (in various signednesses)

This repository provides: