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allow noop tensor expand #67

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allow noop tensor expand #67

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2e8a102
linear bias error example
swfsql Jun 17, 2024
adfc4ac
added IO, improved naming
swfsql Jun 17, 2024
dddd366
reduce to a simple test (failing)
swfsql Jun 20, 2024
59f0e70
ignore expansion if src == dst
swfsql Jun 20, 2024
1987a3b
rm duplicated check
swfsql Jun 20, 2024
8fa3737
simplification
swfsql Jun 20, 2024
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19 changes: 18 additions & 1 deletion src/hl_ops/movement.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -17,7 +17,14 @@ impl<S: Shape> GraphTensor<S> {
S: BroadcastShapeTo<Dst, Ax>,
{
let new_dims = Dst::realized_shape();
if !new_dims.is_empty() {
let src_dims = S::realized_shape();
let is_noop = src_dims.len() == new_dims.len()
&& src_dims
.iter()
.zip(new_dims.iter())
.all(|(src_dim, new_dim)| src_dim.as_num() == new_dim.as_num());

if !new_dims.is_empty() && !is_noop {
for (i, dim) in Ax::as_array().into_iter().map(|i| (i, new_dims[i])) {
self.shape.expand(i, dim);
}
Expand Down Expand Up @@ -514,4 +521,14 @@ mod tests {

assert_close(&c.data(), &d_c.as_vec());
}

#[test]
fn test_noop_expand() {
type S = R1<1>;
type Tensor = GraphTensor<S>;
let mut cx = Graph::new();
let a: Tensor = cx.tensor();
let noop_expanded: Tensor = a.expand::<S, LAxis<0>>();
assert_eq!(a.shape, noop_expanded.shape);
}
}
6 changes: 3 additions & 3 deletions src/hl_ops/reduction.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -96,7 +96,7 @@ mod tests {
let a_data = random_vec(6);
let a = cx.tensor::<R2<2, 3>>();
a.set(a_data.clone());
let b = a.sum_reduce::<_, LAxis<1>>();
let b = a.sum_reduce::<R1<2>, LAxis<1>>();
b.retrieve();

cx.execute();
Expand All @@ -114,7 +114,7 @@ mod tests {
let a_data = random_vec(6);
let a = cx.tensor::<R2<2, 3>>();
a.set(a_data.clone());
let b = a.max_reduce::<_, LAxis<1>>();
let b = a.max_reduce::<R1<2>, LAxis<1>>();
b.retrieve();

cx.execute();
Expand All @@ -132,7 +132,7 @@ mod tests {
let a_data = random_vec(6);
let a = cx.tensor::<R2<2, 3>>();
a.set(a_data.clone());
let b = a.mean_reduce::<_, LAxis<1>>();
let b = a.mean_reduce::<R1<2>, LAxis<1>>();
b.retrieve();

cx.execute();
Expand Down
2 changes: 1 addition & 1 deletion src/shape/broadcast.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@ pub trait ReduceShape<Ax>: Sized + HasAxes<Ax> + ReduceShapeTo<Self::Reduced, Ax
type Reduced: Shape + BroadcastShapeTo<Self, Ax>;
}

impl ReduceShapeTo<(), Axis<0>> for () {}
impl<S: Shape + HasAxes<AnyAxes>, AnyAxes> ReduceShapeTo<S, AnyAxes> for S {}
impl ReduceShape<Axis<0>> for () {
type Reduced = ();
}
Expand Down
28 changes: 21 additions & 7 deletions src/tests/test_prim.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -267,9 +267,15 @@ fn test_sum_reduce() {
let a = cx
.tensor::<R3<2, 2, 3>>()
.set([[[1., 2., 3.], [1., 2., 3.]], [[1., 2., 3.], [1., 2., 3.]]]);
let b = a.sum_reduce::<_, crate::prelude::Axis<1>>().retrieve();
let c = a.sum_reduce::<_, crate::prelude::Axis<0>>().retrieve();
let d = a.sum_reduce::<_, crate::prelude::Axis<2>>().retrieve();
let b = a
.sum_reduce::<R2<2, 3>, crate::prelude::Axis<1>>()
.retrieve();
let c = a
.sum_reduce::<R2<2, 3>, crate::prelude::Axis<0>>()
.retrieve();
let d = a
.sum_reduce::<R2<2, 2>, crate::prelude::Axis<2>>()
.retrieve();
cx.execute();

let d_dev = Cpu::default();
Expand All @@ -290,7 +296,9 @@ fn test_sum_reduce2() {
[[34.4, -96.0, 144.0], [43.0, 560.0, 180.0]],
[[39.6, -120.0, 180.0], [49.5, 700.0, 225.0]],
]]);
let b = a.sum_reduce::<_, crate::prelude::Axis<3>>().retrieve();
let b = a
.sum_reduce::<R3<1, 2, 2>, crate::prelude::Axis<3>>()
.retrieve();
cx.execute();

let d_dev = Cpu::default();
Expand All @@ -309,9 +317,15 @@ fn test_max_reduce() {
let a = cx
.tensor::<R3<2, 2, 3>>()
.set([[[1., 2., 3.], [1., 2., 3.]], [[1., 2., 3.], [1., 2., 3.]]]);
let b = a.max_reduce::<_, crate::prelude::Axis<1>>().retrieve();
let c = a.max_reduce::<_, crate::prelude::Axis<0>>().retrieve();
let d = a.max_reduce::<_, crate::prelude::Axis<2>>().retrieve();
let b = a
.max_reduce::<R2<2, 3>, crate::prelude::Axis<1>>()
.retrieve();
let c = a
.max_reduce::<R2<2, 3>, crate::prelude::Axis<0>>()
.retrieve();
let d = a
.max_reduce::<R2<2, 2>, crate::prelude::Axis<2>>()
.retrieve();
cx.execute();

let d_dev = Cpu::default();
Expand Down