// Batched matrix multiplication: A[N,M,K] @ B[N,K,P] = output[N,M,P].
import storage;
import attributes;
struct PushConstants {
uint left_index;
uint right_index;
uint output_index;
uint batch;
uint rows;
uint inner;
uint columns;
};
[[vk::push_constant]] PushConstants push;
[[vk::binding(0, 0)]] RWByteAddressBuffer storage_buffers[];
[kernel_name("bmm")]
[domain("ml")]
[variant("generic")]
[dtype("f32")]
[status("experimental")]
[shader("compute")]
[numthreads(256, 1, 1)]
void main(uint3 dispatch_thread_id : SV_DispatchThreadID) {
uint index = dispatch_thread_id.x;
uint total = push.batch * push.rows * push.columns;
if (index >= total) {
return;
}
uint matrix_size = push.rows * push.columns;
uint batch_index = index / matrix_size;
uint remainder = index - batch_index * matrix_size;
uint row = remainder / push.columns;
uint column = remainder - row * push.columns;
uint left_base = (batch_index * push.rows + row) * push.inner;
uint right_base = batch_index * push.inner * push.columns + column;
float accumulator = 0.0f;
for (uint k = 0; k < push.inner; ++k) {
accumulator += load_f32(storage_buffers[push.left_index], left_base + k)
* load_f32(storage_buffers[push.right_index], right_base + k * push.columns);
}
store_f32(storage_buffers[push.output_index], index, accumulator);
}