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Add left to right broadcast for Standard binary ops #362

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Merged
merged 8 commits into from
Jul 6, 2021
Merged

Add left to right broadcast for Standard binary ops #362

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254bc10
Add left to right broadcast for binary ops
yanndupis Jul 1, 2021
26493cb
Refactor div broadcast
yanndupis Jul 1, 2021
9c929c3
Rename test to test_div_broadcasting
yanndupis Jul 1, 2021
cdc1fd2
Implement broadcasting for other binary ops
yanndupis Jul 1, 2021
6ca4df7
Add broadcasting for ring binary ops
yanndupis Jul 2, 2021
b983c0a
Add broadcasting to bit ops
yanndupis Jul 2, 2021
6f54f40
remove broadcast for bit and ring binary ops
yanndupis Jul 2, 2021
7be659b
fix merge
yanndupis Jul 6, 2021
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136 changes: 112 additions & 24 deletions rust/moose/src/standard.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -245,13 +245,16 @@ where
{
type Output = StandardTensor<T>;
fn add(self, other: StandardTensor<T>) -> Self::Output {
StandardTensor::<T>(
self.0 + other.0,
HostPlacement {
owner: "TODO".into(),
let placement = HostPlacement {
owner: "TODO".into(),
}
.into();
match self.0.broadcast(other.0.dim()) {
Some(self_broadcasted) => {
StandardTensor::<T>(self_broadcasted.to_owned() + other.0, placement)
}
.into(),
)
None => StandardTensor::<T>(self.0 + other.0, placement),
}
}
}

Expand All @@ -261,13 +264,16 @@ where
{
type Output = StandardTensor<T>;
fn sub(self, other: StandardTensor<T>) -> Self::Output {
StandardTensor::<T>(
self.0 - other.0,
HostPlacement {
owner: "TODO".into(),
let placement = HostPlacement {
owner: "TODO".into(),
}
.into();
match self.0.broadcast(other.0.dim()) {
Some(self_broadcasted) => {
StandardTensor::<T>(self_broadcasted.to_owned() - other.0, placement)
}
.into(),
)
None => StandardTensor::<T>(self.0 - other.0, placement),
}
}
}

Expand All @@ -277,13 +283,16 @@ where
{
type Output = StandardTensor<T>;
fn mul(self, other: StandardTensor<T>) -> Self::Output {
StandardTensor::<T>(
self.0 * other.0,
HostPlacement {
owner: "TODO".into(),
let placement = HostPlacement {
owner: "TODO".into(),
}
.into();
match self.0.broadcast(other.0.dim()) {
Some(self_broadcasted) => {
StandardTensor::<T>(self_broadcasted.to_owned() * other.0, placement)
}
.into(),
)
None => StandardTensor::<T>(self.0 * other.0, placement),
}
}
}

Expand All @@ -293,13 +302,16 @@ where
{
type Output = StandardTensor<T>;
fn div(self, other: StandardTensor<T>) -> Self::Output {
StandardTensor::<T>(
self.0 / other.0,
HostPlacement {
owner: "TODO".into(),
let placement = HostPlacement {
owner: "TODO".into(),
}
.into();
match self.0.broadcast(other.0.dim()) {
Some(self_broadcasted) => {
StandardTensor::<T>(self_broadcasted.to_owned() / other.0, placement)
}
.into(),
)
None => StandardTensor::<T>(self.0 / other.0, placement),
}
}
}

Expand Down Expand Up @@ -489,4 +501,80 @@ mod tests {
assert_eq!(cx, c_exp);
assert_eq!(dx, d_exp);
}

#[test]
fn test_add_broadcasting() {
let x_1 = StandardTensor::<f32>::from(array![2.0].into_dimensionality::<IxDyn>().unwrap());
let y_1 =
StandardTensor::<f32>::from(array![1.0, 2.0].into_dimensionality::<IxDyn>().unwrap());
let z_1 = x_1.add(y_1);
let z_1_exp =
StandardTensor::<f32>::from(array![3.0, 4.0].into_dimensionality::<IxDyn>().unwrap());
let x_2 =
StandardTensor::<f32>::from(array![1.0, 2.0].into_dimensionality::<IxDyn>().unwrap());
let y_2 = StandardTensor::<f32>::from(array![2.0].into_dimensionality::<IxDyn>().unwrap());
let z_2 = x_2.add(y_2);
let z_2_exp =
StandardTensor::<f32>::from(array![3.0, 4.0].into_dimensionality::<IxDyn>().unwrap());

assert_eq!(z_1, z_1_exp);
assert_eq!(z_2, z_2_exp);
}

#[test]
fn test_sub_broadcasting() {
let x_1 = StandardTensor::<f32>::from(array![2.0].into_dimensionality::<IxDyn>().unwrap());
let y_1 =
StandardTensor::<f32>::from(array![1.0, 2.0].into_dimensionality::<IxDyn>().unwrap());
let z_1 = x_1.sub(y_1);
let z_1_exp =
StandardTensor::<f32>::from(array![1.0, 0.0].into_dimensionality::<IxDyn>().unwrap());
let x_2 =
StandardTensor::<f32>::from(array![1.0, 2.0].into_dimensionality::<IxDyn>().unwrap());
let y_2 = StandardTensor::<f32>::from(array![2.0].into_dimensionality::<IxDyn>().unwrap());
let z_2 = x_2.sub(y_2);
let z_2_exp =
StandardTensor::<f32>::from(array![-1.0, 0.0].into_dimensionality::<IxDyn>().unwrap());

assert_eq!(z_1, z_1_exp);
assert_eq!(z_2, z_2_exp);
}

#[test]
fn test_mul_broadcasting() {
let x_1 = StandardTensor::<f32>::from(array![2.0].into_dimensionality::<IxDyn>().unwrap());
let y_1 =
StandardTensor::<f32>::from(array![1.0, 2.0].into_dimensionality::<IxDyn>().unwrap());
let z_1 = x_1.mul(y_1);
let z_1_exp =
StandardTensor::<f32>::from(array![2.0, 4.0].into_dimensionality::<IxDyn>().unwrap());
let x_2 =
StandardTensor::<f32>::from(array![1.0, 2.0].into_dimensionality::<IxDyn>().unwrap());
let y_2 = StandardTensor::<f32>::from(array![2.0].into_dimensionality::<IxDyn>().unwrap());
let z_2 = x_2.mul(y_2);
let z_2_exp =
StandardTensor::<f32>::from(array![2.0, 4.0].into_dimensionality::<IxDyn>().unwrap());

assert_eq!(z_1, z_1_exp);
assert_eq!(z_2, z_2_exp);
}

#[test]
fn test_div_broadcasting() {
let x_1 = StandardTensor::<f32>::from(array![1.0].into_dimensionality::<IxDyn>().unwrap());
let y_1 =
StandardTensor::<f32>::from(array![2.0, 4.0].into_dimensionality::<IxDyn>().unwrap());
let z_1 = x_1.div(y_1);
let z_1_exp =
StandardTensor::<f32>::from(array![0.5, 0.25].into_dimensionality::<IxDyn>().unwrap());
let x_2 =
StandardTensor::<f32>::from(array![2.0, 4.0].into_dimensionality::<IxDyn>().unwrap());
let y_2 = StandardTensor::<f32>::from(array![2.0].into_dimensionality::<IxDyn>().unwrap());
let z_2 = x_2.div(y_2);
let z_2_exp =
StandardTensor::<f32>::from(array![1.0, 2.0].into_dimensionality::<IxDyn>().unwrap());

assert_eq!(z_1, z_1_exp);
assert_eq!(z_2, z_2_exp);
}
}