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Allow conv2d and pool2d to use dynamic dimensions for width and height. #620

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Merged
merged 3 commits into from
Mar 26, 2023
Merged

Allow conv2d and pool2d to use dynamic dimensions for width and height. #620

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04d68f4
allow conv2d and pool2d to use dynamic dimensions
nkoppel Mar 26, 2023
d5e8411
run cargo fmt
nkoppel Mar 26, 2023
513a8b5
check subtraction in convolve_dim for usize
nkoppel Mar 26, 2023
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74 changes: 34 additions & 40 deletions src/tensor_ops/conv2d/mod.rs
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -78,8 +78,10 @@ pub(super) trait Conv2DKernel<E: Dtype>: DeviceStorage {
) -> Result<(), Self::Err>;
}

pub trait ConvAlgebra<const K: usize, const S: usize, const P: usize>: ConstDim {
type Convolved: ConstDim;
pub trait ConvAlgebra<const K: usize, const S: usize, const P: usize>: Dim {
type Convolved: Dim;

fn convolve_dim(&self) -> Self::Convolved;
}

impl<const D: usize, const K: usize, const S: usize, const P: usize> ConvAlgebra<K, S, P>
Expand All @@ -88,6 +90,18 @@ where
Const<{ (D + 2 * P - K) / S + 1 }>: Sized,
{
type Convolved = Const<{ (D + 2 * P - K) / S + 1 }>;

fn convolve_dim(&self) -> Self::Convolved {
Self::Convolved::default()
}
}

impl<const K: usize, const S: usize, const P: usize> ConvAlgebra<K, S, P> for usize {
type Convolved = usize;

fn convolve_dim(&self) -> Self::Convolved {
(self.size() + 2 * P).checked_sub(K).unwrap() / S + 1
}
}

pub trait TryConv2DTo<F, const S: usize, const P: usize>: HasErr {
Expand Down Expand Up @@ -120,42 +134,33 @@ impl<T, F> TryConv2D<F> for T {}

impl<
const C: usize,
const H: usize,
const W: usize,
H: Dim + ConvAlgebra<K, S, P>,
W: Dim + ConvAlgebra<K, S, P>,
const O: usize,
const K: usize,
const S: usize,
const P: usize,
E: Dtype,
D: Conv2DKernel<E> + ZerosTensor<E>,
T: 'static + Tape<E, D>,
> TryConv2DTo<Tensor<Rank4<O, C, K, K>, E, D>, S, P> for Tensor<Rank3<C, H, W>, E, D, T>
where
Const<H>: ConvAlgebra<K, S, P>,
Const<W>: ConvAlgebra<K, S, P>,
> TryConv2DTo<Tensor<Rank4<O, C, K, K>, E, D>, S, P> for Tensor<(Const<C>, H, W), E, D, T>
{
type Output = Tensor<
(
Const<O>,
<Const<H> as ConvAlgebra<K, S, P>>::Convolved,
<Const<W> as ConvAlgebra<K, S, P>>::Convolved,
),
E,
D,
T,
>;
type Output = Tensor<(Const<O>, H::Convolved, W::Convolved), E, D, T>;

fn try_conv2d_to(
self,
filters: Tensor<Rank4<O, C, K, K>, E, D>,
) -> Result<Self::Output, Self::Err> {
let op = Conv2DOp::new(S, P, K, [1, C, H, W], O);
let h = self.shape.1;
let w = self.shape.2;

let op = Conv2DOp::new(S, P, K, [1, C, h.size(), w.size()], O);
let (lhs, ltape) = self.split_tape();
let (rhs, rtape) = filters.split_tape();
let mut tape = ltape.merge(rtape);
let mut out = lhs
.device
.alloc((Const, Default::default(), Default::default()))?;
.alloc((Const, h.convolve_dim(), w.convolve_dim()))?;
lhs.device.forward(op, &lhs, &rhs, &mut out)?;
let phantom_out = out.clone();
tape.try_alloc_grad(&lhs)?;
Expand All @@ -173,43 +178,32 @@ where
impl<
B: Dim,
const C: usize,
const H: usize,
const W: usize,
H: Dim + ConvAlgebra<K, S, P>,
W: Dim + ConvAlgebra<K, S, P>,
const O: usize,
const K: usize,
const S: usize,
const P: usize,
E: Dtype,
D: Conv2DKernel<E> + ZerosTensor<E>,
T: 'static + Tape<E, D>,
> TryConv2DTo<Tensor<Rank4<O, C, K, K>, E, D>, S, P>
for Tensor<(B, Const<C>, Const<H>, Const<W>), E, D, T>
where
Const<H>: ConvAlgebra<K, S, P>,
Const<W>: ConvAlgebra<K, S, P>,
> TryConv2DTo<Tensor<Rank4<O, C, K, K>, E, D>, S, P> for Tensor<(B, Const<C>, H, W), E, D, T>
{
type Output = Tensor<
(
B,
Const<O>,
<Const<H> as ConvAlgebra<K, S, P>>::Convolved,
<Const<W> as ConvAlgebra<K, S, P>>::Convolved,
),
E,
D,
T,
>;
type Output = Tensor<(B, Const<O>, H::Convolved, W::Convolved), E, D, T>;
fn try_conv2d_to(
self,
filters: Tensor<Rank4<O, C, K, K>, E, D>,
) -> Result<Self::Output, Self::Err> {
let batch = self.shape().0;
let op = Conv2DOp::new(S, P, K, [batch.size(), C, H, W], O);
let h = self.shape().2;
let w = self.shape().3;

let op = Conv2DOp::new(S, P, K, [batch.size(), C, h.size(), w.size()], O);
let (lhs, ltape) = self.split_tape();
let (rhs, rtape) = filters.split_tape();
let mut out = lhs
.device
.alloc((batch, Const, Default::default(), Default::default()))?;
.alloc((batch, Const, h.convolve_dim(), w.convolve_dim()))?;
let mut tape = ltape.merge(rtape);
lhs.device.forward(op, &lhs, &rhs, &mut out)?;
let phantom_out = out.clone();
Expand Down
57 changes: 19 additions & 38 deletions src/tensor_ops/pool2d/mod.rs
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -85,37 +85,28 @@ macro_rules! pool2d {

impl<
C: Dim,
const H: usize,
const W: usize,
H: Dim + ConvAlgebra<K, S, P>,
W: Dim + ConvAlgebra<K, S, P>,
E: Dtype,
D: $Kernel<E> + ZerosTensor<E>,
T: 'static + Tape<E, D>,
const K: usize,
const S: usize,
const P: usize,
> $ConstTrait<K, S, P> for Tensor<(C, Const<H>, Const<W>), E, D, T>
where
Const<H>: ConvAlgebra<K, S, P>,
Const<W>: ConvAlgebra<K, S, P>,
> $ConstTrait<K, S, P> for Tensor<(C, H, W), E, D, T>
{
type Output = Tensor<
(
C,
<Const<H> as ConvAlgebra<K, S, P>>::Convolved,
<Const<W> as ConvAlgebra<K, S, P>>::Convolved,
),
E,
D,
T,
>;
type Output = Tensor<(C, H::Convolved, W::Convolved), E, D, T>;

fn try_pool2d(self) -> Result<Self::Output, Self::Err> {
let h = self.shape.1;
let w = self.shape.2;

let &(chan, _, _) = self.shape();
let op = Pool2DOp::new(K, S, P, [1, chan.size(), H, W]);
let op = Pool2DOp::new(K, S, P, [1, chan.size(), h.size(), w.size()]);
let (inp, mut tape) = self.split_tape();
let mut out =
inp.device
.try_zeros_like(&(chan, Default::default(), Default::default()))?;
.try_zeros_like(&(chan, h.convolve_dim(), w.convolve_dim()))?;
inp.device.forward(op, &inp, &mut out)?;
let phantom_out = out.clone();
tape.try_alloc_grad(&inp)?;
Expand All @@ -132,40 +123,30 @@ macro_rules! pool2d {
impl<
B: Dim,
C: Dim,
const H: usize,
const W: usize,
H: Dim + ConvAlgebra<K, S, P>,
W: Dim + ConvAlgebra<K, S, P>,
E: Dtype,
D: $Kernel<E> + ZerosTensor<E>,
T: 'static + Tape<E, D>,
const K: usize,
const S: usize,
const P: usize,
> $ConstTrait<K, S, P> for Tensor<(B, C, Const<H>, Const<W>), E, D, T>
where
Const<H>: ConvAlgebra<K, S, P>,
Const<W>: ConvAlgebra<K, S, P>,
> $ConstTrait<K, S, P> for Tensor<(B, C, H, W), E, D, T>
{
type Output = Tensor<
(
B,
C,
<Const<H> as ConvAlgebra<K, S, P>>::Convolved,
<Const<W> as ConvAlgebra<K, S, P>>::Convolved,
),
E,
D,
T,
>;
type Output = Tensor<(B, C, H::Convolved, W::Convolved), E, D, T>;

fn try_pool2d(self) -> Result<Self::Output, Self::Err> {
let h = self.shape.2;
let w = self.shape.3;

let &(batch, chan, _, _) = self.shape();
let op = Pool2DOp::new(K, S, P, [batch.size(), chan.size(), H, W]);
let op = Pool2DOp::new(K, S, P, [batch.size(), chan.size(), h.size(), w.size()]);
let (inp, mut tape) = self.split_tape();
let mut out = inp.device.try_zeros_like(&(
batch,
chan,
Default::default(),
Default::default(),
h.convolve_dim(),
w.convolve_dim(),
))?;
inp.device.forward(op, &inp, &mut out)?;
let phantom_out = out.clone();
Expand Down
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