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Implement tensor.recip() function to calculate elementwise reciprocals #953

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merged 6 commits into from
Nov 15, 2023
Merged

Implement tensor.recip() function to calculate elementwise reciprocals #953

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55f7a21
Implement tensor.recip() function to calculate elementwise reciprocals
gzsombor Nov 12, 2023
9fef1e5
Add Reciprocal to the supported ONNX operations
gzsombor Nov 13, 2023
aca536d
Update burn-book too
gzsombor Nov 13, 2023
07ed235
Update the supported onnx ops documentation
gzsombor Nov 13, 2023
454d2e7
Add recip ONNX file test
antimora Nov 14, 2023
b55995e
Add autodiff test for recip() call, and fix the implementation
gzsombor Nov 14, 2023
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Add autodiff test for recip() call, and fix the implementation
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@gzsombor
gzsombor committed Nov 15, 2023
commit b55995e6c322c3aefb40fdd00dae211dfeac95a8
19 changes: 14 additions & 5 deletions burn-autodiff/src/ops/tensor.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -440,16 +440,25 @@ impl<B: Backend> TensorOps<Self> for Autodiff<B> {
struct Recip;

impl<B: Backend, const D: usize> Backward<B, D, 1> for Recip {
type State = ();
type State = B::TensorPrimitive<D>;

fn backward(self, ops: Ops<Self::State, 1>, grads: &mut Gradients) {
unary::<B, D, D, _>(ops.parents, ops.node, grads, |grad| B::recip(grad));
let tensor = ops.state;
unary::<B, D, D, _>(ops.parents, ops.node, grads, |grad| {
let tmp = B::powf(tensor, -2.0);
let value = B::neg(tmp);

B::mul(grad, value)
});
}
}

Recip
.prepare([tensor.node], [tensor.graph])
.stateless(B::recip(tensor.primitive))
match Recip.prepare([tensor.node], [tensor.graph]).stateful() {
OpsKind::Tracked(prep) => {
prep.finish(tensor.primitive.clone(), B::recip(tensor.primitive))
}
OpsKind::UnTracked(prep) => prep.finish(B::recip(tensor.primitive)),
}
}

fn swap_dims<const D: usize>(
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2 changes: 2 additions & 0 deletions burn-autodiff/src/tests/mod.rs
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Expand Up @@ -34,6 +34,7 @@ mod mul;
mod multithread;
mod neg;
mod pow;
mod recip;
mod relu;
mod reshape;
mod select;
Expand Down Expand Up @@ -94,6 +95,7 @@ macro_rules! testgen_all {
burn_autodiff::testgen_ad_mul!();
burn_autodiff::testgen_ad_neg!();
burn_autodiff::testgen_ad_powf!();
burn_autodiff::testgen_ad_recip!();
burn_autodiff::testgen_ad_reshape!();
burn_autodiff::testgen_ad_sin!();
burn_autodiff::testgen_ad_softmax!();
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20 changes: 20 additions & 0 deletions burn-autodiff/src/tests/recip.rs
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@@ -0,0 +1,20 @@
#[burn_tensor_testgen::testgen(ad_recip)]
mod tests {
use super::*;
use burn_tensor::Data;

#[test]
fn should_diff_recip() {
let data = Data::from([2.0, 5.0, 0.4]);

let tensor = TestAutodiffTensor::from_data(data).require_grad();
let tensor_out = tensor.clone().recip();

let grads = tensor_out.backward();
let grad = tensor.grad(&grads).unwrap();

assert_eq!(tensor_out.into_data(), Data::from([0.5, 0.2, 2.5]));
grad.to_data()
.assert_approx_eq(&Data::from([-0.25, -0.04, -6.25]), 3);
}
}
1 change: 1 addition & 0 deletions burn-candle/src/lib.rs
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Expand Up @@ -134,6 +134,7 @@ mod tests {
burn_autodiff::testgen_ad_mul!();
burn_autodiff::testgen_ad_neg!();
burn_autodiff::testgen_ad_powf!();
burn_autodiff::testgen_ad_recip!();
burn_autodiff::testgen_ad_reshape!();
burn_autodiff::testgen_ad_sin!();
burn_autodiff::testgen_ad_softmax!();
Expand Down