feat: added slice onnx import

Author: JachymPutta

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

@@ -171,7 +171,7 @@ represent the corresponding Burn Op.
 | [Sin][164]                       |       ✅       |      ✅      |
 | [Sinh][165]                      |       ❌       |      ❌      |
 | [Size][166]                      |       ❌       |      ❌      |
-| [Slice][167]                     |       ❌       |      ✅      |
+| [Slice][167]                     |       ✅       |      ✅      |
 | [Softmax][168]                   |       ✅       |      ✅      |
 | [SoftmaxCrossEntropyLoss][169]   |       ❌       |      ❌      |
 | [Softplus][170]                  |       ❌       |      ❌      |

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

@@ -69,6 +69,7 @@ fn main() {
         .input("tests/conv_transpose2d/conv_transpose2d.onnx")
         .input("tests/pow/pow.onnx")
         .input("tests/pow/pow_int.onnx")
+        .input("tests/slice/slice.onnx")
         .input("tests/sum/sum.onnx")
         .input("tests/sum/sum_int.onnx")
         .input("tests/unsqueeze/unsqueeze.onnx")

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

@@ -71,6 +71,7 @@ include_models!(
     sigmoid,
     sign,
     sin,
+    slice,
     softmax,
     sqrt,
     sub_int,
@@ -459,6 +460,19 @@ mod tests {
         assert!(expected_sum_2d.approx_eq(output_sum_2d, (1.0e-4, 2)));
     }
 
+    #[test]
+    fn slice() {
+        let model: slice::Model<Backend> = slice::Model::default();
+        let device = Default::default();
+
+        let input =
+            Tensor::<Backend, 1>::from_floats([1., 2., 3., 4., 5., 6., 7., 8., 9., 10.], &device);
+        let output = model.forward(input);
+        let expected = Data::from([1., 2., 3., 4., 5.]);
+
+        assert_eq!(output.to_data(), expected);
+    }
+
     #[test]
     fn softmax() {
         // Initialize the model without weights (because the exported file does not contain them)

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

@@ -0,0 +1,101 @@
+#!/usr/bin/env python3
+
+# used to generate model: onnx-tests/tests/slice/slice.onnx
+
+import onnx
+from onnx import helper, TensorProto
+
+def main() -> None:
+    # Starts
+    starts_val = [0]  # Example shape value
+    starts_tensor = helper.make_tensor(
+        name="starts",
+        data_type=TensorProto.INT64,
+        dims=[len(starts_val)],
+        vals=starts_val,
+    )
+    starts_node = helper.make_node(
+        "Constant",
+        name="starts_constant",
+        inputs=[],
+        outputs=["starts"],
+        value=starts_tensor,
+    )
+
+    # Ends
+    ends_val = [5]  # Example shape value
+    ends_tensor = helper.make_tensor(
+        name="ends",
+        data_type=TensorProto.INT64,
+        dims=[len(ends_val)],
+        vals=ends_val,
+    )
+    ends_node = helper.make_node(
+        "Constant",
+        name="ends_constant",
+        inputs=[],
+        outputs=["ends"],
+        value=ends_tensor,
+    )
+
+    # Axes
+    axes_val = [0]  # Example shape value
+    axes_tensor = helper.make_tensor(
+        name="axes",
+        data_type=TensorProto.INT64,
+        dims=[len(axes_val)],
+        vals=axes_val,
+    )
+    axes_node = helper.make_node(
+        "Constant",
+        name="axes_constant",
+        inputs=[],
+        outputs=["axes"],
+        value=axes_tensor,
+    )
+
+    # Steps
+    steps_val = [1]  # Example shape value
+    steps_tensor = helper.make_tensor(
+        name="steps",
+        data_type=TensorProto.INT64,
+        dims=[len(steps_val)],
+        vals=steps_val,
+    )
+    steps_node = helper.make_node(
+        "Constant",
+        name="steps_constant",
+        inputs=[],
+        outputs=["steps"],
+        value=steps_tensor,
+    )
+
+    # Define the Slice node that uses the outputs from the constant nodes
+    slice_node = helper.make_node(
+        "Slice",
+        name="slice_node",
+        inputs=["input_tensor", "starts", "ends", "axes", "steps"],
+        outputs=["output"],
+    )
+
+    # Create the graph
+    graph_def = helper.make_graph(
+        nodes=[starts_node, ends_node, axes_node, steps_node, slice_node],
+        name="SliceGraph",
+        inputs=[
+            helper.make_tensor_value_info("input_tensor", TensorProto.FLOAT, [10]),
+        ],
+        outputs=[
+            helper.make_tensor_value_info("output", TensorProto.FLOAT, [5])
+        ],
+    )
+
+    # Create the model
+    model_def = helper.make_model(graph_def, producer_name="slice")
+
+    # Save the model to a file
+    onnx.save(model_def, "slice.onnx")
+
+
+if __name__ == "__main__":
+    main()

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

@@ -7,7 +7,7 @@ use super::{
     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, sum::SumNode, unary::UnaryNode,
+    reshape::ReshapeNode, slice::SliceNode, squeeze::SqueezeNode, sum::SumNode, unary::UnaryNode,
     unsqueeze::UnsqueezeNode,
 };
 use crate::burn::{BurnImports, Scope, Type};
@@ -102,6 +102,7 @@ pub enum Node<PS: PrecisionSettings> {
     MaxPool2d(MaxPool2dNode),
     Range(RangeNode),
     Reshape(ReshapeNode),
+    Slice(SliceNode),
     Squeeze(SqueezeNode),
     Sum(SumNode),
     Unary(UnaryNode),
@@ -139,6 +140,7 @@ macro_rules! match_all {
             Node::MaxPool2d(node) => $func(node),
             Node::Range(node) => $func(node),
             Node::Reshape(node) => $func(node),
+            Node::Slice(node) => $func(node),
             Node::Squeeze(node) => $func(node),
             Node::Sum(node) => $func(node),
             Node::Unary(node) => $func(node),
@@ -186,6 +188,7 @@ impl<PS: PrecisionSettings> Node<PS> {
             Node::MaxPool2d(_) => "max_pool2d",
             Node::Range(_) => "range",
             Node::Reshape(_) => "reshape",
+            Node::Slice(_) => "slice",
             Node::Squeeze(_) => "squeeze",
             Node::Sum(_) => "add",
             Node::Unary(unary) => unary.kind.as_str(),

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

@@ -27,6 +27,7 @@ pub(crate) mod random_normal;
 pub(crate) mod random_uniform;
 pub(crate) mod range;
 pub(crate) mod reshape;
+pub(crate) mod slice;
 pub(crate) mod squeeze;
 pub(crate) mod sum;
 pub(crate) mod unary;

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

@@ -0,0 +1,104 @@
+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 SliceNode {
+    pub input: TensorType,
+    pub output: TensorType,
+    pub starts: Vec<usize>,
+    pub ends: Vec<usize>,
+    // pub axes: Option<Vec<i64>>,
+    // pub steps: Option<Vec<i64>>,
+}
+
+impl<PS: PrecisionSettings> NodeCodegen<PS> for SliceNode {
+    fn output_types(&self) -> Vec<Type> {
+        vec![Type::Tensor(self.output.clone())]
+    }
+    fn input_types(&self) -> Vec<Type> {
+        vec![Type::Tensor(self.input.clone())]
+    }
+    fn forward(&self, scope: &mut Scope, node_position: usize) -> TokenStream {
+        let input = scope.tensor_use_owned(&self.input, node_position);
+        let output = &self.output.name;
+        // let axes = match &self.axes {
+        //     Some(axes) => axes.to_tokens(),
+        //     None => quote! { None },
+        // };
+        // let steps = match &self.steps {
+        //     Some(steps) => steps.to_tokens(),
+        //     None => quote! { None },
+        // };
+
+        // compine starts and ends into ranges
+        // example: starts = [0, 0, 0, 0], ends = [1, 1, 1, 1]
+        // ranges = [0..1, 0..1, 0..1, 0..1]
+        let starts = &self.starts;
+        let ends = &self.ends;
+
+        quote! {
+            let #output = #input.slice([#(#starts..#ends),*]);
+        }
+    }
+    fn into_node(self) -> Node<PS> {
+        Node::Slice(self)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use burn::record::FullPrecisionSettings;
+
+    use super::*;
+    use crate::burn::{
+        graph::BurnGraph,
+        node::{slice::SliceNode, test::assert_tokens},
+        TensorType,
+    };
+
+    #[test]
+    fn test_codegen_slice() {
+        let mut graph = BurnGraph::<FullPrecisionSettings>::default();
+        graph.register(SliceNode::new(
+            TensorType::new_float("tensor1", 4),
+            TensorType::new_float("tensor2", 4),
+            vec![0, 0, 0, 0],
+            vec![1, 1, 1, 1],
+        ));
+        graph.register_input_output(vec!["tensor1".to_string()], vec!["tensor2".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>) -> Tensor<B, 4> {
+                    let tensor2 = tensor1.slice([0usize..1usize,0usize..1usize,0usize..1usize,0usize..1usize]);
+
+                    tensor2
+                }
+            }
+        };
+
+        assert_tokens(graph.codegen(), expected);
+    }
+}

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

@@ -64,6 +64,7 @@ pub fn dim_inference(node: &mut Node, graph_io: &mut OnnxGraphIO) {
         NodeType::Sigmoid => same_as_input(node),
         NodeType::Sign => same_as_input(node),
         NodeType::Sin => same_as_input(node),
+        NodeType::Slice => slice_update_outputs(node),
         NodeType::Softmax => same_as_input(node),
         NodeType::Sqrt => same_as_input(node),
         NodeType::Sub => same_as_input(node),
@@ -426,6 +427,33 @@ fn squeeze_update_output(node: &mut Node) {
     });
 }
 
+fn slice_update_outputs(node: &mut Node) {
+    let shape = match &node.inputs[1].value {
+        Some(value) => match value {
+            Data::Int64s(shape) => Some(shape.clone()),
+            _ => panic!("Slice: invalid input types"),
+        },
+        None => None,
+    };
+
+    if shape.is_none() {
+        panic!("Slice: invalid shape");
+    }
+
+    let output = match &node.outputs[0].ty {
+        ArgType::Tensor(tensor) => tensor.clone(),
+        _ => panic!("Slice: invalid output types"),
+    };
+
+    if let Some(shape) = shape {
+        node.outputs[0].ty = ArgType::Tensor(TensorType {
+            dim: shape.len(),
+            shape: None, // shape is calculated at runtime
+            ..output
+        });
+    }
+}
+
 /// Update the output tensor dimension based on the "axes" attribute or the second input
 fn unsqueeze_update_output(node: &mut Node) {
     let axes = if node.inputs.len() == 2 {

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

@@ -17,7 +17,7 @@ use super::ir::{ArgType, Argument, Node, NodeType};
 
 use protobuf::Message;
 
-const LIFT_CONSTANTS_FOR_NODE_TYPES: [NodeType; 10] = [
+const LIFT_CONSTANTS_FOR_NODE_TYPES: [NodeType; 11] = [
     NodeType::BatchNormalization,
     NodeType::Clip,
     NodeType::Conv1d,
@@ -27,6 +27,7 @@ const LIFT_CONSTANTS_FOR_NODE_TYPES: [NodeType; 10] = [
     NodeType::Reshape,
     NodeType::Unsqueeze,
     NodeType::ReduceSum,
+    NodeType::Slice,
     NodeType::Squeeze,
 ];