cubecl_std/tensor/
identity.rs1use cubecl::frontend::TensorHandleRef;
2use cubecl::prelude::*;
3use cubecl::tensor_line_size_parallel;
4use cubecl_core as cubecl;
5
6use super::TensorHandle;
7
8#[cube(launch_unchecked)]
9fn identity_kernel<C: Numeric>(
10 output: &mut Tensor<Line<C>>,
11 gap: usize,
12 #[define(C)] _elem: StorageType,
13) {
14 let pos_x = ABSOLUTE_POS_X as usize * output.line_size();
15 let pos_y = ABSOLUTE_POS_Y as usize;
16 if pos_y < output.shape(0) && pos_x < output.shape(1) {
17 let mut line = Line::empty(output.line_size()).fill(C::from_int(0));
18 let offs_y = pos_y * output.stride(0);
19
20 let start_pos = offs_y + pos_x;
21 let mut offset = 0;
22 while offset < output.line_size() {
23 let remainder = (start_pos + offset) % gap;
24 if remainder == 0 {
25 line[offset] = C::from_int(1);
26 offset += gap;
27 } else {
28 offset += gap - remainder;
29 }
30 }
31 output[start_pos / output.line_size()] = line;
32 }
33}
34
35pub fn launch<R: Runtime>(client: &ComputeClient<R>, output: &TensorHandle<R>) {
39 let dtype = output.dtype;
40 launch_ref(client, &output.as_ref(), dtype);
41}
42
43pub fn launch_ref<R: Runtime>(
47 client: &ComputeClient<R>,
48 output: &TensorHandleRef<R>,
49 dtype: StorageType,
50) {
51 assert_eq!(2, output.shape.len(), "input should be a matrix");
52 assert_eq!(
53 output.shape[0], output.shape[1],
54 "input should be a square matrix"
55 );
56
57 let vectorization_factor = tensor_line_size_parallel(
58 R::supported_line_sizes().iter().cloned(),
59 output.shape,
60 output.strides,
61 1,
62 );
63
64 let cube_dim = CubeDim::new_2d(16, 16);
65 let lines_x = output.shape[1] as u32 / vectorization_factor as u32;
66 let cube_count_x = lines_x.div_ceil(cube_dim.x);
67 let cube_count_y = (output.shape[0] as u32).div_ceil(cube_dim.y);
68 let cube_count = CubeCount::new_2d(cube_count_x, cube_count_y);
69
70 unsafe {
71 identity_kernel::launch_unchecked(
72 client,
73 cube_count,
74 cube_dim,
75 TensorArg::from_raw_parts_and_size(
76 output.handle,
77 output.strides,
78 output.shape,
79 vectorization_factor,
80 dtype.size(),
81 ),
82 ScalarArg::new(output.strides[0] + 1),
83 dtype,
84 )
85 .expect("Should be able to launch the kernel all the time")
86 }
87}