feat: add sum onnx import

crates/burn-import/SUPPORTED-ONNX-OPS.md

@@ -184,7 +184,7 @@ represent the corresponding Burn Op.
 | [STFT][177]                      |       ❌       |      ❌      |
 | [StringNormalizer][178]          |       ❌       |      ❌      |
 | [Sub][179]                       |       ✅       |      ✅      |
-| [Sum][180]                       |       ❌       |      ✅      |
+| [Sum][180]                       |       ✅       |      ✅      |
 | [Tan][181]                       |       ❌       |      ❌      |
 | [Tanh][182]                      |       ✅       |      ✅      |
 | [TfIdfVectorizer][183]           |       ❌       |      ❌      |

crates/burn-import/onnx-tests/build.rs

@@ -68,6 +68,8 @@ fn main() {
         .input("tests/conv_transpose2d/conv_transpose2d.onnx")
         .input("tests/pow/pow.onnx")
         .input("tests/pow/pow_int.onnx")
+        .input("tests/sum/sum.onnx")
+        .input("tests/sum/sum_int.onnx")
         .input("tests/unsqueeze/unsqueeze.onnx")
         .input("tests/unsqueeze/unsqueeze_opset16.onnx")
         .input("tests/unsqueeze/unsqueeze_opset11.onnx")

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

@@ -74,6 +74,8 @@ include_models!(
     sqrt,
     sub_int,
     sub,
+    sum,
+    sum_int,
     tanh,
     transpose,
     conv_transpose2d,
@@ -160,6 +162,36 @@ mod tests {
         assert_eq!(output.to_data(), expected);
     }
 
+    #[test]
+    fn sum_tensor_and_tensor() {
+        let device = Default::default();
+        let model: sum::Model<Backend> = sum::Model::default();
+
+        let input1 = Tensor::<Backend, 1>::from_floats([1., 2., 3., 4.], &device);
+        let input2 = Tensor::<Backend, 1>::from_floats([1., 2., 3., 4.], &device);
+        let input3 = Tensor::<Backend, 1>::from_floats([1., 2., 3., 4.], &device);
+
+        let output = model.forward(input1, input2, input3);
+        let expected = Data::from([3., 6., 9., 12.]);
+
+        assert_eq!(output.to_data(), expected);
+    }
+
+    #[test]
+    fn sum_int_tensor_and_int_tensor() {
+        let device = Default::default();
+        let model: sum_int::Model<Backend> = sum_int::Model::default();
+
+        let input1 = Tensor::<Backend, 1, Int>::from_ints([1, 2, 3, 4], &device);
+        let input2 = Tensor::<Backend, 1, Int>::from_ints([1, 2, 3, 4], &device);
+        let input3 = Tensor::<Backend, 1, Int>::from_ints([1, 2, 3, 4], &device);
+
+        let output = model.forward(input1, input2, input3);
+        let expected = Data::from([3, 6, 9, 12]);
+
+        assert_eq!(output.to_data(), expected);
+    }
+
     #[test]
     fn mul_scalar_with_tensor_and_tensor_with_tensor() {
         // Initialize the model with weights (loaded from the exported file)

crates/burn-import/onnx-tests/tests/sum/sum.onnx

@@ -0,0 +1,22 @@
+
+	sum-model:�

+%
+input1
+input2
+input3output"SumSumGraphZ
+input1
+
+

+Z
+input2
+
+

+Z
+input3
+
+

+b
+output
+
+

+B

crates/burn-import/onnx-tests/tests/sum/sum.py

@@ -0,0 +1,41 @@
+#!/usr/bin/env python3
+
+# used to generate model: onnx-tests/tests/sum/sum.onnx
+
+import onnx
+import onnx.helper
+import onnx.checker
+import numpy as np
+
+# Create input tensors
+input1 = onnx.helper.make_tensor_value_info('input1', onnx.TensorProto.FLOAT, [3])
+input2 = onnx.helper.make_tensor_value_info('input2', onnx.TensorProto.FLOAT, [3])
+input3 = onnx.helper.make_tensor_value_info('input3', onnx.TensorProto.FLOAT, [3])
+
+# Create output tensor
+output = onnx.helper.make_tensor_value_info('output', onnx.TensorProto.FLOAT, [3])
+
+# Create the Sum node
+sum_node = onnx.helper.make_node(
+    'Sum',
+    inputs=['input1', 'input2', 'input3'],
+    outputs=['output']
+)
+
+# Create the graph (GraphProto)
+graph_def = onnx.helper.make_graph(
+    nodes=[sum_node],
+    name='SumGraph',
+    inputs=[input1, input2, input3],
+    outputs=[output]
+)
+
+# Create the model (ModelProto)
+model_def = onnx.helper.make_model(graph_def, producer_name='sum-model')
+onnx.checker.check_model(model_def)
+
+# Save the ONNX model
+onnx.save(model_def, 'sum.onnx')
+
+print("ONNX model 'sum.onnx' generated successfully.")
+

crates/burn-import/onnx-tests/tests/sum/sum_int.onnx

@@ -0,0 +1,22 @@
+
+	sum-model:�

+%
+input1
+input2
+input3output"SumSumGraphZ
+input1
+
+
+Z
+input2
+
+
+Z
+input3
+
+
+b
+output
+
+
+B

crates/burn-import/onnx-tests/tests/sum/sum_int.py

@@ -0,0 +1,40 @@
+#!/usr/bin/env python3
+
+# used to generate model: onnx-tests/tests/sum/sum.onnx
+
+import onnx
+import onnx.helper
+import onnx.checker
+import numpy as np
+
+# Create input tensors
+input1 = onnx.helper.make_tensor_value_info('input1', onnx.TensorProto.INT64, [3])
+input2 = onnx.helper.make_tensor_value_info('input2', onnx.TensorProto.INT64, [3])
+input3 = onnx.helper.make_tensor_value_info('input3', onnx.TensorProto.INT64, [3])
+
+# Create output tensor
+output = onnx.helper.make_tensor_value_info('output', onnx.TensorProto.INT64, [3])
+
+# Create the Sum node
+sum_node = onnx.helper.make_node(
+    'Sum',
+    inputs=['input1', 'input2', 'input3'],
+    outputs=['output']
+)
+
+# Create the graph (GraphProto)
+graph_def = onnx.helper.make_graph(
+    nodes=[sum_node],
+    name='SumGraph',
+    inputs=[input1, input2, input3],
+    outputs=[output]
+)
+
+# Create the model (ModelProto)
+model_def = onnx.helper.make_model(graph_def, producer_name='sum-model')
+onnx.checker.check_model(model_def)
+
+# Save the ONNX model
+onnx.save(model_def, 'sum_int.onnx')
+
+print("ONNX model 'sum_int.onnx' generated successfully.")

crates/burn-import/src/burn/node/base.rs

@@ -1,14 +1,13 @@
-use super::expand::ExpandNode;
 use super::{
     argmax::ArgMaxNode, avg_pool1d::AvgPool1dNode, avg_pool2d::AvgPool2dNode,
     batch_norm::BatchNormNode, binary::BinaryNode, clip::ClipNode, concat::ConcatNode,
     constant::ConstantNode, conv1d::Conv1dNode, conv2d::Conv2dNode,
-    conv_transpose_2d::ConvTranspose2dNode, dropout::DropoutNode, gather::GatherNode,
-    global_avg_pool::GlobalAvgPoolNode, layer_norm::LayerNormNode, linear::LinearNode,
-    mask_where::WhereNode, matmul::MatmulNode, max_pool1d::MaxPool1dNode,
+    conv_transpose_2d::ConvTranspose2dNode, dropout::DropoutNode, expand::ExpandNode,
+    gather::GatherNode, global_avg_pool::GlobalAvgPoolNode, layer_norm::LayerNormNode,
+    linear::LinearNode, mask_where::WhereNode, matmul::MatmulNode, max_pool1d::MaxPool1dNode,
     max_pool2d::MaxPool2dNode, prelu::PReluNode, random_normal::RandomNormalNode,
     random_uniform::RandomUniformNode, range::RangeNode, reshape::ReshapeNode,
-    squeeze::SqueezeNode, unary::UnaryNode, unsqueeze::UnsqueezeNode,
+    squeeze::SqueezeNode, sum::SumNode, unary::UnaryNode, unsqueeze::UnsqueezeNode,
 };
 use crate::burn::{BurnImports, Scope, Type};
 use burn::backend::NdArray;
@@ -102,6 +101,7 @@ pub enum Node<PS: PrecisionSettings> {
     Range(RangeNode),
     Reshape(ReshapeNode),
     Squeeze(SqueezeNode),
+    Sum(SumNode),
     Unary(UnaryNode),
     Unsqueeze(UnsqueezeNode),
     Where(WhereNode),
@@ -137,6 +137,7 @@ macro_rules! match_all {
             Node::Range(node) => $func(node),
             Node::Reshape(node) => $func(node),
             Node::Squeeze(node) => $func(node),
+            Node::Sum(node) => $func(node),
             Node::Unary(node) => $func(node),
             Node::Unsqueeze(node) => $func(node),
             Node::Where(node) => $func(node),
@@ -182,6 +183,7 @@ impl<PS: PrecisionSettings> Node<PS> {
             Node::Range(_) => "range",
             Node::Reshape(_) => "reshape",
             Node::Squeeze(_) => "squeeze",
+            Node::Sum(_) => "add",
             Node::Unary(unary) => unary.kind.as_str(),
             Node::Unsqueeze(_) => "unsqueeze",
             Node::Where(_) => "where",

crates/burn-import/src/burn/node/mod.rs

@@ -27,6 +27,7 @@ pub(crate) mod random_uniform;
 pub(crate) mod range;
 pub(crate) mod reshape;
 pub(crate) mod squeeze;
+pub(crate) mod sum;
 pub(crate) mod unary;
 pub(crate) mod unsqueeze;
 pub(crate) use base::*;

crates/burn-import/src/burn/node/sum.rs

@@ -0,0 +1,108 @@
+use super::{Node, NodeCodegen};
+use crate::burn::{Scope, TensorType, Type};
+
+use burn::record::PrecisionSettings;
+use proc_macro2::TokenStream;
+use quote::quote;
+
+#[derive(Debug, Clone, new)]
+pub struct SumNode {
+    pub inputs: Vec<TensorType>,
+    pub output: TensorType,
+}
+
+impl<PS: PrecisionSettings> NodeCodegen<PS> for SumNode {
+    fn output_types(&self) -> Vec<Type> {
+        vec![Type::Tensor(self.output.clone())]
+    }
+
+    fn input_types(&self) -> Vec<Type> {
+        self.inputs
+            .iter()
+            .map(|t| Type::Tensor(t.clone()))
+            .collect()
+    }
+
+    fn forward(&self, scope: &mut Scope, node_position: usize) -> TokenStream {
+        let inputs = self
+            .inputs
+            .iter()
+            .map(|t| scope.tensor_use_owned(t, node_position));
+
+        let output = &self.output.name;
+
+        quote! {
+            let #output = #(#inputs)+*;
+        }
+    }
+
+    fn into_node(self) -> Node<PS> {
+        Node::Sum(self)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use burn::record::FullPrecisionSettings;
+
+    use super::*;
+    use crate::burn::{
+        graph::BurnGraph,
+        node::{sum::SumNode, test::assert_tokens},
+        TensorType,
+    };
+
+    #[test]
+    fn test_codegen_concat() {
+        let mut graph = BurnGraph::<FullPrecisionSettings>::default();
+
+        graph.register(SumNode::new(
+            vec![
+                TensorType::new_float("tensor1", 4),
+                TensorType::new_float("tensor2", 4),
+            ],
+            TensorType::new_float("tensor3", 4),
+        ));
+
+        graph.register_input_output(
+            vec!["tensor1".to_string(), "tensor2".to_string()],
+            vec!["tensor3".to_string()],
+        );
+
+        let expected = quote! {
+            use burn::{
+                module::Module,
+                tensor::{backend::Backend, Tensor},
+            };
+
+            #[derive(Module, Debug)]
+            pub struct Model<B: Backend> {
+                phantom: core::marker::PhantomData<B>,
+                device: burn::module::Ignored<B::Device>,
+            }
+
+            impl<B: Backend> Model <B> {
+                #[allow(unused_variables)]
+                pub fn new(device: &B::Device) -> Self {
+                    Self {
+                        phantom: core::marker::PhantomData,
+                        device: burn::module::Ignored(device.clone()),
+                    }
+                }
+
+                #[allow(clippy::let_and_return, clippy::approx_constant)]
+                pub fn forward(
+                    &self,
+                    tensor1: Tensor<B, 4>,
+                    tensor2: Tensor<B, 4>
+                ) -> Tensor<B, 4> {
+                    let tensor3 = tensor1 + tensor2;
+
+                    tensor3
+                }
+            }
+        };
+
+        assert_tokens(graph.codegen(), expected);
+    }
+}

crates/burn-import/src/onnx/dim_inference.rs

@@ -66,6 +66,7 @@ pub fn dim_inference(node: &mut Node, graph_io: &mut OnnxGraphIO) {
         NodeType::Softmax => same_as_input(node),
         NodeType::Sqrt => same_as_input(node),
         NodeType::Sub => same_as_input(node),
+        NodeType::Sum => same_as_input(node),
         NodeType::Tanh => same_as_input(node),
         NodeType::Transpose => same_as_input(node),
         NodeType::Unsqueeze => unsqueeze_update_output(node),

crates/burn-import/src/onnx/to_burn.rs

@@ -42,6 +42,7 @@ use crate::{
             range::RangeNode,
             reshape::ReshapeNode,
             squeeze::SqueezeNode,
+            sum::SumNode,
             unary::UnaryNode,
             unsqueeze::UnsqueezeNode,
         },
@@ -291,6 +292,7 @@ impl OnnxGraph {
                 NodeType::Shape => graph.register(Self::shape_conversion(node)),
                 NodeType::Sigmoid => graph.register(Self::sigmoid_conversion(node)),
                 NodeType::Sin => graph.register(Self::sin_conversion(node)),
+                NodeType::Sum => graph.register(Self::sum_conversion(node)),
                 NodeType::Transpose => graph.register(Self::transpose_conversion(node)),
                 NodeType::Concat => graph.register(Self::concat_conversion(node)),
                 NodeType::Cast => graph.register(Self::cast_conversion(node)),
@@ -673,6 +675,17 @@ impl OnnxGraph {
         UnaryNode::sin(input, output)
     }
 
+    fn sum_conversion(node: Node) -> SumNode {
+        let inputs = node
+            .inputs
+            .iter()
+            .map(|input| input.to_tensor_type())
+            .collect();
+        let output = node.outputs.first().unwrap().to_tensor_type();
+
+        SumNode::new(inputs, output)
+    }
+
     fn reciprocal_conversion(node: Node) -> UnaryNode {
         let input = node.inputs.first().unwrap().to_type();
         let output = node.outputs.first().unwrap().to_type();