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Use MatrixBuffers for encode_gemm. Do not create result buffer within. #3

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merged 3 commits into from
Oct 31, 2023
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Use MatrixBuffers for encode_gemm. Do not create result buffer within. #3

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36ae080
Ditch generic Matrix and use MatrixBuffer instead. Create buffers sep…
ivarflakstad Oct 31, 2023
7910f10
Mark encode_gemm c parameter as &mut
ivarflakstad Oct 31, 2023
aaf2647
Return Result from encode_gemm
ivarflakstad Oct 31, 2023
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117 changes: 71 additions & 46 deletions examples/mps/matrix-multiplication/main.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -15,12 +15,15 @@ fn main() {

fn correctness() {
// First verify the correctness of the naive solution
let a = Matrix::new([1, 2, 6, 24, 120, 720], 3, 2);
let b = Matrix::new([1, 2, 3, 5, 8, 13], 2, 3);
let result = matrix_mul::<Int32>(a, b);
let m = 3;
let n = 3;
let k = 2;
let a = vec![1, 2, 6, 24, 120, 720];
let b = vec![1, 2, 6, 24, 120, 720];
let result = matrix_mul::<Int32>(a, b, m, n, k);
assert_eq!(
result.entries(),
&[11, 18, 29, 126, 204, 330, 3720, 6000, 9720]
result,
&[49, 242, 1446, 582, 2892, 17316, 17400, 86640, 519120]
);

const M: u64 = 100;
Expand All @@ -35,33 +38,42 @@ fn correctness() {
for i in 0..ITERATIONS {
progress_bar(i, ITERATIONS);

let left = generate_matrix::<Float32, M, K>();
let right = generate_matrix::<Float32, K, N>();
let a = generate_matrix::<Float32, M, K>(&device);
let b = generate_matrix::<Float32, K, N>(&device);
let mut c = generate_matrix::<Float32, K, N>(&device);

let command_buffer = command_queue.new_command_buffer();
let result = encode_gemm(
encode_gemm(
&device,
command_buffer,
false,
false,
&left,
&right,
&a,
&b,
&mut c,
1.0,
0.0,
);
)
.expect("Encoding failed");
command_buffer.commit();
command_buffer.wait_until_completed();

let expected = matrix_mul(left, right);
approx_eq(result.contents(), expected.entries().to_vec());
let expected = matrix_mul::<Float32>(
a.contents(),
b.contents(),
M as usize,
K as usize,
N as usize,
);
approx_eq(c.contents(), expected);
}

println!(" ✅\n");
}

fn short_type_name<T>() -> String {
let name = type_name::<T>();
let mut parts = name.split("::");
let parts = name.split("::");
parts.last().unwrap().to_string()
}

Expand All @@ -77,18 +89,19 @@ fn performance() {

println!("Performance: ");

let a = short_type_name::<A>();
let b = short_type_name::<B>();
let c = short_type_name::<C>();
println!("{M}x{K}x{a} * {K}x{N}x{b} = {M}x{N}x{c}");
println!("Generating input matrices...");
// Generate random matrices
let left = generate_matrix::<A, M, K>();
let right = generate_matrix::<B, K, N>();
let a_tname = short_type_name::<A>();
let b_tname = short_type_name::<B>();
let c_tname = short_type_name::<C>();
println!("{M}x{K}x{a_tname} * {K}x{N}x{b_tname} = {M}x{N}x{c_tname}");

// Setup
let device = Device::system_default().expect("No device found");

println!("Generating input matrices...");
// Generate random matrices
let a = generate_matrix::<A, M, K>(&device);
let b = generate_matrix::<B, K, N>(&device);
let mut c = generate_matrix::<C, K, N>(&device);

let cases = [
(false, false, 1.0, 0.0),
(true, false, 1.0, 0.0),
Expand All @@ -105,16 +118,18 @@ fn performance() {
for i in 0..ITERATIONS {
progress_bar(i, ITERATIONS);

let _: MatrixBuffer<C> = encode_gemm(
encode_gemm(
&device,
command_buffer,
t_left,
t_right,
&left,
&right,
&a,
&b,
&mut c,
alpha,
beta,
);
)
.expect("Encoding failed");
}
command_buffer.commit();
command_buffer.wait_until_completed();
Expand All @@ -135,44 +150,54 @@ fn performance() {
}
}

fn generate_matrix<T, const ROWS: u64, const COLS: u64>() -> Matrix<T>
fn generate_matrix<T, const ROWS: u64, const COLS: u64>(device: &Device) -> MatrixBuffer<T>
where
T: MPSDataType,
GEMMInput<T>: Valid,
{
let mut rng = thread_rng();
Matrix::new(
(0..ROWS * COLS).map(|_| T::from_f64(rng.gen())),
ROWS as NSUInteger,
COLS as NSUInteger,
)

// Create descriptors for the matrices.
let row_bytes_for_columns = MatrixDescriptor::row_bytes_for_columns(COLS, T::TYPE_ID);

// Create buffers
let options = MTLResourceOptions::StorageModeShared;
let data = (0..ROWS * COLS)
.map(|_| T::from_f64(rng.gen()))
.collect::<Vec<T::Type>>();
let buffer =
device.new_buffer_with_data(data.as_ptr().cast(), ROWS * row_bytes_for_columns, options);

MatrixBuffer::from_buffer(buffer, ROWS, COLS)
}

// Naive matrix multiplication for testing
fn matrix_mul<T: MPSDataType>(a: Matrix<T>, b: Matrix<T>) -> Matrix<T>
fn matrix_mul<T: MPSDataType>(
a: Vec<T::Type>,
b: Vec<T::Type>,
m: usize,
n: usize,
k: usize,
) -> Vec<T::Type>
where
T::Type: AddAssign + Mul<Output = T::Type> + Copy,
{
assert_eq!(a.columns(), b.rows());
let sum_count = a.columns() as usize;
let rows = a.rows() as usize;
let columns = b.columns() as usize;
let size = rows * columns;
let size = m * n;

let mut entries = Vec::with_capacity(size);
let mut c = Vec::with_capacity(size);

for idx in 0..size {
let i = idx / rows;
let j = idx % columns;
let i = idx / m;
let j = idx % n;

let mut sum = T::from_f64(0.0);
for di in 0..sum_count {
sum += a.entry(i, di) * b.entry(di, j);
for di in 0..k {
sum += a[(i * k) + di] * b[(di * n) + j];
}
entries.push(sum);
c.push(sum);
}

Matrix::new(entries, a.rows(), b.columns())
c
}

fn euclidean_distance<T>(a: Vec<T>, b: Vec<T>) -> f64
Expand Down
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