#![allow(non_snake_case)]
use teeny_core::dtype::Num;
use teeny_macros::kernel;
use teeny_triton::triton::{
types::{AddOffsets, Comparison, Tensor},
*,
};
#[kernel]
pub fn maxpool3d_forward<
T: Triton,
D: Num,
const KD: i32,
const KH: i32,
const KW: i32,
const STRIDE_D: i32,
const STRIDE_H: i32,
const STRIDE_W: i32,
const BLOCK_OW: i32,
>(
input_ptr: T::Pointer<D>,
output_ptr: T::Pointer<D>,
_B: i32,
C: i32,
Dv: i32,
H: i32,
W: i32,
OD: i32,
OH: i32,
OW: i32,
) where
T::I32Tensor: Tensor<i32, 1>,
T::I32Tensor: Comparison<i32, BoolTensor = T::BoolTensor>,
T::Pointer<D>: AddOffsets<i32, 1, T::I32Tensor, Output = T::Tensor<T::Pointer<D>>>,
{
let pid = T::program_id(Axis::X);
let num_ow_tiles = T::cdiv(OW, BLOCK_OW);
let ow_tile = pid % num_ow_tiles;
let rest = pid / num_ow_tiles;
let oh = rest % OH;
let rest2 = rest / OH;
let od = rest2 % OD;
let bco = rest2 / OD;
let c = bco % C;
let b = bco / C;
let ow_start = ow_tile * BLOCK_OW;
let ow_range = T::arange(0, BLOCK_OW) + ow_start;
let ow_mask = ow_range.lt(OW);
let in_bc_base = (b * C + c) * Dv * H * W;
let out_base = ((b * C + c) * OD * OH * OW) + od * OH * OW + oh * OW;
let mut acc = T::cast::<f32, D>(T::full::<f32>(&[BLOCK_OW], -3.4028235e38_f32), None, false);
let loop_bound = KD * KH * KW;
for idx in 0..loop_bound {
let kw = idx % KW;
let tmp = idx / KW;
let kh = tmp % KH;
let kd = tmp / KH;
let id = od * STRIDE_D + kd;
let ih = oh * STRIDE_H + kh;
let iw_range = ow_range * STRIDE_W + kw;
let in_offsets = iw_range + (in_bc_base + id * H * W + ih * W);
let tile = T::load(
input_ptr.add_offsets(in_offsets),
Some(ow_mask),
Some(T::cast::<f32, D>(
T::full::<f32>(&[BLOCK_OW], -3.4028235e38_f32),
None,
false,
)),
&[],
None,
None,
None,
false,
);
acc = T::maximum(acc, tile);
}
let out_offsets = ow_range + out_base;
T::store(
output_ptr.add_offsets(out_offsets),
acc,
Some(ow_mask),
&[],
None,
None,
);
}
#[kernel]
pub fn maxpool3d_backward<
T: Triton,
D: Num,
const KD: i32,
const KH: i32,
const KW: i32,
const STRIDE_D: i32,
const STRIDE_H: i32,
const STRIDE_W: i32,
const BLOCK_OW: i32,
>(
dy_ptr: T::Pointer<D>,
x_ptr: T::Pointer<D>,
y_ptr: T::Pointer<D>,
dx_ptr: T::Pointer<D>,
_B: i32,
C: i32,
Dv: i32,
H: i32,
W: i32,
OD: i32,
OH: i32,
OW: i32,
) where
T::I32Tensor: Tensor<i32, 1>,
T::I32Tensor: Comparison<i32, BoolTensor = T::BoolTensor>,
T::Pointer<D>: AddOffsets<i32, 1, T::I32Tensor, Output = T::Tensor<T::Pointer<D>>>,
{
let pid = T::program_id(Axis::X);
let num_ow_tiles = T::cdiv(OW, BLOCK_OW);
let ow_tile = pid % num_ow_tiles;
let rest = pid / num_ow_tiles;
let oh = rest % OH;
let rest2 = rest / OH;
let od = rest2 % OD;
let bco = rest2 / OD;
let c = bco % C;
let b = bco / C;
let ow_start = ow_tile * BLOCK_OW;
let ow_range = T::arange(0, BLOCK_OW) + ow_start;
let ow_mask = ow_range.lt(OW);
let in_bc_base = (b * C + c) * Dv * H * W;
let out_base = ((b * C + c) * OD * OH * OW) + od * OH * OW + oh * OW;
let out_offsets = ow_range + out_base;
let dy_tile = T::load(
dy_ptr.add_offsets(out_offsets),
Some(ow_mask),
Some(T::zeros::<D>(&[BLOCK_OW])),
&[],
None,
None,
None,
false,
);
let y_tile = T::load(
y_ptr.add_offsets(out_offsets),
Some(ow_mask),
Some(T::zeros::<D>(&[BLOCK_OW])),
&[],
None,
None,
None,
false,
);
let loop_bound = KD * KH * KW;
for idx in 0..loop_bound {
let kw = idx % KW;
let tmp = idx / KW;
let kh = tmp % KH;
let kd = tmp / KH;
let id = od * STRIDE_D + kd;
let ih = oh * STRIDE_H + kh;
let iw_range = ow_range * STRIDE_W + kw;
let in_offsets = iw_range + (in_bc_base + id * H * W + ih * W);
let x_tile = T::load(
x_ptr.add_offsets(in_offsets),
Some(ow_mask),
Some(T::cast::<f32, D>(
T::full::<f32>(&[BLOCK_OW], -3.4028235e38_f32),
None,
false,
)),
&[],
None,
None,
None,
false,
);
let is_max = T::eq(x_tile, y_tile);
let grad = T::where_(is_max, dy_tile, T::zeros::<D>(&[BLOCK_OW]));
T::atomic_add(
dx_ptr.add_offsets(in_offsets),
grad,
Some(ow_mask),
None,
None,
);
}
}
pub struct Maxpool3dOp<'a, T: Num> {
pub forward: Maxpool3dForward<T>,
pub backward: Maxpool3dBackward<T>,
_marker: core::marker::PhantomData<&'a ()>,
}