Implement tensor.recip() function to calculate elementwise reciprocals

burn-autodiff/src/ops/tensor.rs

@@ -435,6 +435,32 @@ impl<B: Backend> TensorOps<Self> for Autodiff<B> {
             .stateless(B::neg(tensor.primitive))
     }
 
+    fn recip<const D: usize>(tensor: FloatTensor<Self, D>) -> FloatTensor<Self, D> {
+        #[derive(Debug)]
+        struct Recip;
+
+        impl<B: Backend, const D: usize> Backward<B, D, 1> for Recip {
+            type State = B::TensorPrimitive<D>;
+
+            fn backward(self, ops: Ops<Self::State, 1>, grads: &mut Gradients) {
+                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)
+                });
+            }
+        }
+
+        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>(
         tensor: FloatTensor<Self, D>,
         dim1: usize,

burn-autodiff/src/tests/mod.rs

@@ -34,6 +34,7 @@ mod mul;
 mod multithread;
 mod neg;
 mod pow;
+mod recip;
 mod relu;
 mod reshape;
 mod select;
@@ -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!();

burn-autodiff/src/tests/recip.rs

@@ -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);
+    }
+}

burn-book/src/building-blocks/tensor.md

@@ -126,6 +126,7 @@ Those operations are only available for `Float` tensors.
 | `tensor.erf()`                                      | `tensor.erf()`                     |
 | `tensor.powf(value)`                                | `tensor.pow(value)`                |
 | `tensor.sqrt()`                                     | `tensor.sqrt()`                    |
+| `tensor.recip()`                                    | `tensor.reciprocal()`              |
 | `tensor.cos()`                                      | `tensor.cos()`                     |
 | `tensor.sin()`                                      | `tensor.sin()`                     |
 | `tensor.tanh()`                                     | `tensor.tanh()`                    |

burn-candle/src/lib.rs

@@ -56,6 +56,7 @@ mod tests {
     burn_tensor::testgen_arg!();
     burn_tensor::testgen_cast!();
     burn_tensor::testgen_cat!();
+    burn_tensor::testgen_recip!();
     burn_tensor::testgen_clamp!();
     burn_tensor::testgen_cos!();
     // burn_tensor::testgen_div!();
@@ -133,6 +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!();

burn-candle/src/ops/tensor.rs

@@ -442,4 +442,8 @@ impl<F: FloatCandleElement, I: IntCandleElement> TensorOps<Self> for Candle<F, I
     ) -> FloatTensor<Self, D> {
         CandleTensor::new(tensor.tensor.clamp(min, max).unwrap())
     }
+
+    fn recip<const D: usize>(tensor: FloatTensor<Self, D>) -> FloatTensor<Self, D> {
+        CandleTensor::new(tensor.tensor.recip().unwrap())
+    }
 }

burn-fusion/src/graph/ops.rs

@@ -100,6 +100,11 @@ pub enum FloatOpsDescription<B: FusionBackend> {
         (TensorDescription, Distribution<FloatElem<B>>),
         Box<dyn Ops<B, Args = (TensorDescription, Distribution<FloatElem<B>>)>>,
     ),
+    /// Operation corresponding to [recip](burn_tensor::ops::TensorOps::recip).
+    Recip(
+        UnaryOpsDescription,
+        Box<dyn Ops<B, Args = UnaryOpsDescription>>,
+    ),
 }
 
 /// Operation description specific to module.
@@ -1252,6 +1257,7 @@ impl<B: FusionBackend> FloatOpsDescription<B> {
             FloatOpsDescription::Log(desc, _) => handles.cleanup(&desc.input),
             FloatOpsDescription::Log1p(desc, _) => handles.cleanup(&desc.input),
             FloatOpsDescription::Erf(desc, _) => handles.cleanup(&desc.input),
+            FloatOpsDescription::Recip(desc, _) => handles.cleanup(&desc.input),
             FloatOpsDescription::Powf(desc, _) => handles.cleanup(&desc.lhs),
             FloatOpsDescription::Sqrt(desc, _) => handles.cleanup(&desc.input),
             FloatOpsDescription::Cos(desc, _) => handles.cleanup(&desc.input),
@@ -1268,6 +1274,7 @@ impl<B: FusionBackend> FloatOpsDescription<B> {
             FloatOpsDescription::Log(desc, ops) => ops.execute(desc, handles),
             FloatOpsDescription::Log1p(desc, ops) => ops.execute(desc, handles),
             FloatOpsDescription::Erf(desc, ops) => ops.execute(desc, handles),
+            FloatOpsDescription::Recip(desc, ops) => ops.execute(desc, handles),
             FloatOpsDescription::Powf(desc, ops) => ops.execute(desc, handles),
             FloatOpsDescription::Sqrt(desc, ops) => ops.execute(desc, handles),
             FloatOpsDescription::Cos(desc, ops) => ops.execute(desc, handles),

burn-fusion/src/ops/float.rs

@@ -1391,6 +1391,21 @@ impl<B: FusionBackend> TensorOps<Self> for Fusion<B> {
         out
     }
 
+    fn recip<const D: usize>(tensor: FloatTensor<Self, D>) -> FloatTensor<Self, D> {
+        unary_float_ops!(Recip, B::recip);
+
+        let out = tensor.client.create_tensor_empty(tensor.shape.clone());
+        out.client
+            .register(TensorOpsDescription::FloatOps(FloatOpsDescription::Recip(
+                UnaryOpsDescription {
+                    input: tensor.into_description(),
+                    out: out.to_description_out(),
+                },
+                Box::new(Recip::<D>),
+            )));
+        out
+    }
+
     fn erf<const D: usize>(tensor: FloatTensor<Self, D>) -> FloatTensor<Self, D> {
         unary_float_ops!(TanhOps, B::erf);
 

burn-import/SUPPORTED-ONNX-OPS.md

@@ -134,7 +134,7 @@ represent the corresponding Burn Op.
 | [RandomUniform][128]             |       ❌       |      ✅      |
 | [RandomUniformLike][129]         |       ❌       |      ✅      |
 | [Range][130]                     |       ❌       |      ✅      |
-| [Reciprocal][131]                |       ❌       |      ❌      |
+| [Reciprocal][131]                |       ✅       |      ✅      |
 | [ReduceL][132]                   |       ❌       |      ❌      |
 | [ReduceLogSum][133]              |       ❌       |      ❌      |
 | [ReduceLogSumExp][134]           |       ❌       |      ❌      |

burn-import/onnx-tests/build.rs

@@ -27,6 +27,7 @@ fn main() {
         .input("tests/log_softmax/log_softmax.onnx")
         .input("tests/maxpool2d/maxpool2d.onnx")
         .input("tests/mul/mul.onnx")
+        .input("tests/recip/recip.onnx")
         .input("tests/relu/relu.onnx")
         .input("tests/reshape/reshape.onnx")
         .input("tests/sigmoid/sigmoid.onnx")

burn-import/onnx-tests/tests/onnx_tests.rs

@@ -34,6 +34,7 @@ include_models!(
     log_softmax,
     maxpool2d,
     mul,
+    recip,
     relu,
     reshape,
     sigmoid,
@@ -591,4 +592,17 @@ mod tests {
         let expected = Data::from([[[[0.7616, 0.9640, 0.9951, 0.9993]]]]);
         output.to_data().assert_approx_eq(&expected, 4);
     }
+
+    #[test]
+    fn recip() {
+        // Initialize the model
+        let model = recip::Model::<Backend>::new();
+
+        // Run the model
+        let input = Tensor::<Backend, 4>::from_floats([[[[1., 2., 3., 4.]]]]);
+        let output = model.forward(input);
+        // data from pyTorch
+        let expected = Data::from([[[[1.0000, 0.5000, 0.3333, 0.2500]]]]);
+        output.to_data().assert_approx_eq(&expected, 4);
+    }
 }

burn-import/onnx-tests/tests/recip/recip.onnx

@@ -0,0 +1,17 @@
+pytorch2.1.0:�

+0
+onnx::Reciprocal_0
1/Reciprocal"
+Reciprocal
+main_graphZ,
+onnx::Reciprocal_0
+

+

+
+
+b
+
1
+

+

+
+
+B

burn-import/onnx-tests/tests/recip/recip.py

@@ -0,0 +1,42 @@
+#!/usr/bin/env python3
+
+# used to generate model: onnx-tests/tests/recip/recip.onnx
+
+import torch
+import torch.nn as nn
+
+
+class Model(nn.Module):
+    def __init__(self):
+        super(Model, self).__init__()
+
+    def forward(self, x):
+        return x.reciprocal()
+
+
+def main():
+    # Set random seed for reproducibility
+    torch.manual_seed(0)
+
+    # Export to onnx
+    model = Model()
+    model.eval()
+    device = torch.device("cpu")
+    onnx_name = "recip.onnx"
+    dummy_input = torch.randn(1, 2, 3, 4, device=device)
+
+    torch.onnx.export(model, (dummy_input), onnx_name,
+                      verbose=False, opset_version=16)
+
+    print("Finished exporting model to {}".format(onnx_name))
+
+    # Output some test data for use in the test
+    test_input = torch.tensor([[[[1.0, 2.0, 3.0, 4.0]]]])
+
+    print("Test input data: {}".format(test_input))
+    output = model.forward(test_input)
+    print("Test output data: {}".format(output))
+
+
+if __name__ == '__main__':
+    main()

burn-import/src/burn/node/unary.rs

@@ -26,6 +26,7 @@ pub enum UnaryNodeKind {
     LogSoftmax,
     Softmax,
     Relu,
+    Reciprocal,
     Sigmoid,
     Tanh,
     Transpose,
@@ -40,6 +41,7 @@ impl UnaryNodeKind {
             Self::LogSoftmax => "log_softmax",
             Self::Softmax => "softmax",
             Self::Relu => "relu",
+            Self::Reciprocal => "reciprocal",
             Self::Sigmoid => "sigmoid",
             Self::Tanh => "tanh",
             Self::Transpose => "transpose",
@@ -141,6 +143,11 @@ impl UnaryNode {
         Self::new(input, output, UnaryNodeKind::Transpose, Rc::new(function))
     }
 
+    pub(crate) fn reciprocal(input: Type, output: Type) -> Self {
+        let function = move |input| quote! { #input.recip() };
+        Self::new(input, output, UnaryNodeKind::Reciprocal, Rc::new(function))
+    }
+
     /// Casts the input to the output type.
     ///
     /// Currently this function only supports the following conversions:
@@ -334,6 +341,25 @@ mod tests {
         );
     }
 
+    #[test]
+    fn test_unary_codegen_reciprocal() {
+        one_node_graph(
+            UnaryNode::reciprocal(
+                Type::Tensor(TensorType::new_float("tensor1", 4)),
+                Type::Tensor(TensorType::new_float("tensor2", 4)),
+            ),
+            quote! {
+                pub fn forward(&self, tensor1: Tensor<B, 4>) -> Tensor<B, 4> {
+                    let tensor2 = tensor1.recip();
+
+                    tensor2
+                }
+            },
+            vec!["tensor1".to_string()],
+            vec!["tensor2".to_string()],
+        );
+    }
+
     #[test]
     fn test_unary_codegen_cast() {
         one_node_graph(

burn-import/src/onnx/dim_inference.rs

@@ -79,6 +79,7 @@ pub fn dim_inference(
             NodeType::Erf => same_as_input(node),
             NodeType::Sqrt => same_as_input(node),
             NodeType::Tanh => same_as_input(node),
+            NodeType::Reciprocal => same_as_input(node),
             NodeType::Softmax => same_as_input(node),
             NodeType::ReduceMean => mean_update_outputs(node),
             NodeType::Constant => constant_update_outputs(node),

burn-import/src/onnx/to_burn.rs

@@ -250,6 +250,7 @@ impl ONNXGraph {
                 NodeType::Tanh => graph.register(Self::tanh_conversion(node)),
                 NodeType::Constant => graph.register(Self::constant_conversion::<PS>(node)),
                 NodeType::Reshape => graph.register(Self::reshape_conversion(node)),
+                NodeType::Reciprocal => graph.register(Self::reciprocal_conversion(node)),
                 NodeType::Sigmoid => graph.register(Self::sigmoid_conversion(node)),
                 NodeType::Transpose => graph.register(Self::transpose_conversion(node)),
                 NodeType::Concat => graph.register(Self::concat_conversion(node)),
@@ -447,6 +448,13 @@ impl ONNXGraph {
         UnaryNode::sigmoid(input, output)
     }
 
+    fn reciprocal_conversion(node: Node) -> UnaryNode {
+        let input = node.inputs.get(0).unwrap().to_type();
+        let output = node.outputs.get(0).unwrap().to_type();
+
+        UnaryNode::reciprocal(input, output)
+    }
+
     fn log_softmax_conversion(node: Node) -> UnaryNode {
         let input = node.inputs.get(0).unwrap().to_type();
         let output = node.outputs.get(0).unwrap().to_type();

burn-ndarray/src/ops/base.rs

@@ -181,6 +181,13 @@ where
         NdArrayTensor { array }
     }
 
+    pub fn recip<const D: usize>(tensor: NdArrayTensor<E, D>) -> NdArrayTensor<E, D> {
+        let array = tensor.array.map(|x| 1.elem::<E>() / *x);
+        let array = array.into_shared();
+
+        NdArrayTensor { array }
+    }
+
     pub fn mean<const D: usize>(tensor: NdArrayTensor<E, D>) -> NdArrayTensor<E, 1> {
         let data = Data::from([tensor.array.mean().unwrap()]);
         NdArrayTensor::from_data(data)

burn-ndarray/src/ops/tensor.rs

@@ -127,6 +127,10 @@ impl<E: FloatNdArrayElement> TensorOps<Self> for NdArray<E> {
         Self::mul_scalar(tensor, (-1f32).elem::<E>())
     }
 
+    fn recip<const D: usize>(tensor: NdArrayTensor<E, D>) -> NdArrayTensor<E, D> {
+        NdArrayMathOps::recip(tensor)
+    }
+
     fn swap_dims<const D: usize>(
         tensor: NdArrayTensor<E, D>,
         dim1: usize,

burn-tch/src/ops/tensor.rs

@@ -172,6 +172,10 @@ impl<E: TchElement> TensorOps<Self> for LibTorch<E> {
         Self::mul_scalar(tensor, (-1f32).elem::<E>())
     }
 
+    fn recip<const D: usize>(tensor: TchTensor<E, D>) -> TchTensor<E, D> {
+        TchTensor::new(tensor.tensor.reciprocal())
+    }
+
     fn swap_dims<const D: usize>(
         tensor: TchTensor<E, D>,
         dim1: usize,

burn-tensor/src/tensor/api/float.rs

@@ -67,6 +67,11 @@ where
         Self::new(B::powf(self.primitive, value))
     }
 
+    /// Applies element wise reciprocal operation.
+    pub fn recip(self) -> Self {
+        Self::new(B::recip(self.primitive))
+    }
+
     /// Applies element wise root square operation.
     pub fn sqrt(self) -> Self {
         Self::new(B::sqrt(self.primitive))

burn-tensor/src/tensor/ops/tensor.rs

@@ -410,6 +410,9 @@ pub trait TensorOps<B: Backend> {
         Self::mul_scalar(tensor, (-1.0_f32).elem::<FloatElem<B>>())
     }
 
+    /// Calculates the reciprocals elementwise
+    fn recip<const D: usize>(tensor: FloatTensor<B, D>) -> FloatTensor<B, D>;
+
     /// Transposes a tensor.
     ///
     /// # Arguments