Crates.io | microgemm |
lib.rs | microgemm |
version | 0.2.0 |
source | src |
created_at | 2023-10-29 11:34:09.710809 |
updated_at | 2024-03-04 21:51:17.301144 |
description | General matrix multiplication with custom configuration in Rust. Supports no_std and no_alloc environments. |
homepage | |
repository | https://github.com/cospectrum/microgemm |
max_upload_size | |
id | 1017466 |
size | 267,163 |
General matrix multiplication with custom configuration in Rust.
Supports no_std
and no_alloc
environments.
The implementation is based on the BLIS microkernel approach.
cargo add microgemm
The Kernel
trait is the main abstraction of microgemm
.
You can implement it yourself or use kernels that are already provided out of the box.
use microgemm::{kernels::GenericKernel8x8, Kernel as _, MatMut, MatRef, PackSizes};
fn main() {
let kernel = GenericKernel8x8::<f32>::new();
assert_eq!(kernel.mr(), 8);
assert_eq!(kernel.nr(), 8);
let pack_sizes = PackSizes {
mc: 5 * kernel.mr(), // MC must be divisible by MR
kc: 190,
nc: 9 * kernel.nr(), // NC must be divisible by NR
};
let mut packing_buf = vec![0.0; pack_sizes.buf_len()];
let (alpha, beta) = (2.0, -3.0);
let (m, k, n) = (100, 380, 250);
let a = vec![2.0; m * k];
let b = vec![3.0; k * n];
let mut c = vec![4.0; m * n];
let a = MatRef::row_major(m, k, &a);
let b = MatRef::row_major(k, n, &b);
let mut c = MatMut::row_major(m, n, &mut c);
// c <- alpha a b + beta c
kernel.gemm(alpha, &a, &b, beta, &mut c, &pack_sizes, &mut packing_buf);
println!("{:?}", c.as_slice());
}
Also see no_alloc example for use without Vec
.
Name | Scalar Types | Target |
---|---|---|
GenericKernelNxN (N: 2, 4, 8, 16, 32) |
T: Copy + Zero + One + Mul + Add | Any |
NeonKernel4x4 | f32 | aarch64 and target feature neon |
NeonKernel8x8 | f32 | aarch64 and target feature neon |
use microgemm::{typenum::U4, Kernel, MatMut, MatRef};
struct CustomKernel;
impl Kernel for CustomKernel {
type Scalar = f64;
type Mr = U4;
type Nr = U4;
// dst <- alpha lhs rhs + beta dst
fn microkernel(
&self,
alpha: f64,
lhs: &MatRef<f64>,
rhs: &MatRef<f64>,
beta: f64,
dst: &mut MatMut<f64>,
) {
// lhs is col-major
assert_eq!(lhs.row_stride(), 1);
assert_eq!(lhs.nrows(), Self::MR);
// rhs is row-major
assert_eq!(rhs.col_stride(), 1);
assert_eq!(rhs.ncols(), Self::NR);
// dst is col-major
assert_eq!(dst.row_stride(), 1);
assert_eq!(dst.nrows(), Self::MR);
assert_eq!(dst.ncols(), Self::NR);
// your microkernel implementation...
}
}
All benchmarks are performed in a single thread
on square matrices of dimension n
.
PackSizes { mc: n, kc: n, nc: n }
n NeonKernel8x8 faer matrixmultiply
128 75.5µs 242.6µs 46.2µs
256 466.3µs 3.2ms 518.2µs
512 3ms 15.9ms 2.7ms
1024 23.9ms 128.4ms 22ms
2048 191ms 1s 182.8ms
Licensed under either of Apache License, Version 2.0 or MIT license at your option.