Add ONNX op Random Normal Like

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

@@ -133,7 +133,7 @@ represent the corresponding Burn Op.
 | [QLinearMatMul][124]             |       ❌       |      ❌      |
 | [QuantizeLinear][125]            |       ❌       |      ❌      |
 | [RandomNormal][126]              |       ✅       |      ✅      |
-| [RandomNormalLike][127]          |       ❌       |      ✅      |
+| [RandomNormalLike][127]          |       ✅       |      ✅      |
 | [RandomUniform][128]             |       ✅       |      ✅      |
 | [RandomUniformLike][129]         |       ❌       |      ✅      |
 | [Range][130]                     |       ✅       |      ✅      |

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

@@ -75,6 +75,7 @@ fn main() {
         .input("tests/pow/pow_int.onnx")
         .input("tests/prelu/prelu.onnx")
         .input("tests/random_normal/random_normal.onnx")
+        .input("tests/random_normal_like/random_normal_like.onnx")
         .input("tests/random_uniform/random_uniform.onnx")
         .input("tests/range/range.onnx")
         .input("tests/recip/recip.onnx")

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

@@ -0,0 +1,15 @@
+pytorch2.2.0:�

+P
+onnx::RandomNormalLike_0
1/RandomNormalLike"RandomNormalLike*
+dtype
�

+main_graphZ.
+onnx::RandomNormalLike_0
+

+
+
+b
+
1
+

+
+
+B

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

@@ -0,0 +1,49 @@
+#!/usr/bin/env python3
+
+# used to generate model: random_normal_like.onnx
+
+import torch
+import torch.nn as nn
+
+
+class RandomNormalLikeModel(nn.Module):
+    def __init__(self):
+        super(RandomNormalLikeModel, self).__init__()
+
+    def forward(self, x):
+        return torch.randn_like(x)
+
+
+def main():
+    # Set seed for reproducibility
+    torch.manual_seed(42)
+
+    # Set print options for better precision output
+    torch.set_printoptions(precision=8)
+
+    # Export Random NormalLike Model
+    model = RandomNormalLikeModel()
+    model.eval()
+    device = torch.device("cpu")
+
+    # Generate test input: a 2D matrix or batch of 2D matrices
+    file_name = "random_normal_like.onnx"
+    test_input = torch.randn(2, 4, 4, device=device)  # 2 batches of 4x4 matrices
+    torch.onnx.export(model,
+                      test_input,
+                      file_name,
+                      verbose=False,
+                      opset_version=16)
+
+    print("Finished exporting model to {}".format(file_name))
+
+    # Output some test data for use in the test
+    print("Test input data: {}".format(test_input))
+    print("Test input data shape: {}".format(test_input.shape))
+    output = model.forward(test_input)
+    print("Test output data shape: {}".format(output.shape))
+    print("Test output: {}".format(output))
+
+
+if __name__ == '__main__':
+    main()

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

@@ -84,6 +84,7 @@ include_models!(
     pow_int,
     prelu,
     random_normal,
+    random_normal_like,
     random_uniform,
     range,
     recip,
@@ -2157,6 +2158,18 @@ mod tests {
         assert_eq!(expected_shape, output.shape());
     }
 
+    #[test]
+    fn random_normal_like() {
+        let device = Default::default();
+        let model = random_normal_like::Model::<Backend>::new(&device);
+        let input = TensorData::zeros::<f64, _>(Shape::from([2, 4, 4]));
+        let expected_shape = Shape::from([2, 4, 4]);
+
+        let output = model.forward(input.into());
+
+        assert_eq!(expected_shape, output.shape());
+    }
+
     #[test]
     fn constant_of_shape() {
         // This tests shape is being passed directly to the model

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

@@ -10,10 +10,10 @@ use super::{
     gather_elements::GatherElementsNode, global_avg_pool::GlobalAvgPoolNode,
     layer_norm::LayerNormNode, linear::LinearNode, mask_where::WhereNode, matmul::MatmulNode,
     max_pool1d::MaxPool1dNode, max_pool2d::MaxPool2dNode, mean::MeanNode, pad::PadNode,
-    prelu::PReluNode, random_normal::RandomNormalNode, random_uniform::RandomUniformNode,
-    range::RangeNode, reshape::ReshapeNode, resize::ResizeNode, slice::SliceNode,
-    squeeze::SqueezeNode, sum::SumNode, tile::TileNode, trilu::TriluNode, unary::UnaryNode,
-    unsqueeze::UnsqueezeNode,
+    prelu::PReluNode, random_normal::RandomNormalNode, random_normal_like::RandomNormalLikeNode,
+    random_uniform::RandomUniformNode, range::RangeNode, reshape::ReshapeNode, resize::ResizeNode,
+    slice::SliceNode, squeeze::SqueezeNode, sum::SumNode, tile::TileNode, trilu::TriluNode,
+    unary::UnaryNode, unsqueeze::UnsqueezeNode,
 };
 use crate::burn::{BurnImports, Scope, Type};
 use burn::backend::NdArray;
@@ -121,8 +121,9 @@ pub enum Node<PS: PrecisionSettings> {
     Unary(UnaryNode),
     Unsqueeze(UnsqueezeNode),
     Where(WhereNode),
-    RandomUniform(RandomUniformNode),
     RandomNormal(RandomNormalNode),
+    RandomNormalLike(RandomNormalLikeNode),
+    RandomUniform(RandomUniformNode),
     ConstantOfShape(ConstantOfShapeNode),
     // For now, we have to keep the precision settings in order to correctly serialize the fields
     // into the right data types.
@@ -172,6 +173,7 @@ macro_rules! match_all {
             Node::Unsqueeze(node) => $func(node),
             Node::Where(node) => $func(node),
             Node::RandomNormal(node) => $func(node),
+            Node::RandomNormalLike(node) => $func(node),
             Node::RandomUniform(node) => $func(node),
             Node::ConstantOfShape(node) => $func(node),
             _ => unimplemented!(),
@@ -230,6 +232,7 @@ impl<PS: PrecisionSettings> Node<PS> {
             Node::Unsqueeze(_) => "unsqueeze",
             Node::Where(_) => "where",
             Node::RandomNormal(_) => "random_normal",
+            Node::RandomNormalLike(_) => "random_normal_like",
             Node::RandomUniform(_) => "random_uniform",
             Node::ConstantOfShape(_) => "constant_of_shape",
             _ => unimplemented!(),

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

@@ -30,6 +30,7 @@ pub(crate) mod mean;
 pub(crate) mod pad;
 pub(crate) mod prelu;
 pub(crate) mod random_normal;
+pub(crate) mod random_normal_like;
 pub(crate) mod random_uniform;
 pub(crate) mod range;
 pub(crate) mod reshape;

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

@@ -0,0 +1,101 @@
+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 RandomNormalLikeNode {
+    pub mean: f64,
+    pub scale: f64,
+    pub input: TensorType,
+    pub output: TensorType,
+}
+
+impl RandomNormalLikeNode {
+    // Set distribution parameters based on mean and scale
+    fn get_distribution(&self) -> TokenStream {
+        let mean = self.mean;
+        let std_deviation = self.scale;
+        quote! { Distribution::Normal(#mean, #std_deviation) }
+    }
+}
+
+impl<PS: PrecisionSettings> NodeCodegen<PS> for RandomNormalLikeNode {
+    fn input_types(&self) -> Vec<Type> {
+        vec![Type::Tensor(self.input.clone())]
+    }
+
+    fn output_types(&self) -> Vec<Type> {
+        vec![Type::Tensor(self.output.clone())]
+    }
+
+    fn forward(&self, _scope: &mut Scope, _node_position: usize) -> TokenStream {
+        let output = &self.output.name;
+        let input = &self.input.name;
+        let dist = self.get_distribution();
+        quote! {
+            let #output = #input.random_like(#dist);
+        }
+    }
+
+    fn into_node(self) -> Node<PS> {
+        Node::RandomNormalLike(self)
+    }
+
+    fn register_imports(&self, imports: &mut crate::burn::BurnImports) {
+        imports.register("burn::tensor::Distribution");
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    use crate::burn::{graph::BurnGraph, node::test::assert_tokens, TensorKind, TensorType};
+    use burn::record::FullPrecisionSettings;
+
+    #[test]
+    fn test_random_normal_like_codegen() {
+        let mut graph = BurnGraph::<FullPrecisionSettings>::default();
+
+        graph.register(RandomNormalLikeNode::new(
+            0.0f64,
+            1.0f64,
+            TensorType::new("input", 2, TensorKind::Float, Some(vec![2, 3])),
+            TensorType::new("output", 2, TensorKind::Float, Some(vec![2, 3])),
+        ));
+
+        graph.register_input_output(vec!["input".to_string()], vec!["output".to_string()]);
+
+        let expected = quote! {
+            use burn::tensor::Distribution;
+            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, input: Tensor<B, 2>) -> Tensor<B, 2> {
+                    let output = input.random_like(Distribution::Normal(0f64, 1f64));
+                    output
+                }
+            }
+        };
+
+        assert_tokens(graph.codegen(), expected);
+    }
+}

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

@@ -44,6 +44,7 @@ use crate::{
             pad::PadNode,
             prelu::PReluNode,
             random_normal::RandomNormalNode,
+            random_normal_like::RandomNormalLikeNode,
             random_uniform::RandomUniformNode,
             range::RangeNode,
             reshape::ReshapeNode,
@@ -345,6 +346,9 @@ impl ParsedOnnxGraph {
                 NodeType::Tile => graph.register(Self::tile_conversion(node)),
                 NodeType::Trilu => graph.register(Self::trilu_conversion(node)),
                 NodeType::RandomNormal => graph.register(Self::random_normal_conversion(node)),
+                NodeType::RandomNormalLike => {
+                    graph.register(Self::random_normal_like_conversion(node))
+                }
                 NodeType::ConstantOfShape => {
                     graph.register(Self::constant_of_shape_conversion(node))
                 }
@@ -472,6 +476,27 @@ impl ParsedOnnxGraph {
         RandomNormalNode::new(output_type, mean, scale)
     }
 
+    fn random_normal_like_conversion(node: Node) -> RandomNormalLikeNode {
+        let input = TensorType::from(node.inputs.first().unwrap());
+        let output = TensorType::from(node.outputs.first().unwrap());
+        let mean = node
+            .attrs
+            .get("mean")
+            .map(|val| val.clone().into_f32() as f64)
+            .unwrap_or(0.0f64);
+        let scale = node
+            .attrs
+            .get("scale")
+            .map(|val| val.clone().into_f32() as f64)
+            .unwrap_or(1.0f64);
+
+        if node.attrs.contains_key("seed") {
+            warn!("seed attribute is not supported!");
+        }
+
+        RandomNormalLikeNode::new(mean, scale, input, output)
+    }
+
     pub(crate) fn constant_of_shape_conversion(node: Node) -> ConstantOfShapeNode {
         // Additional types needed for ConstantOfShape:
         use crate::burn::node::constant_of_shape::ConstantValue;

crates/onnx-ir/src/dim_inference.rs

@@ -61,6 +61,7 @@ pub fn dim_inference(node: &mut Node) {
         NodeType::PRelu => same_as_input_broadcast(node),
         NodeType::Pow => same_as_input_broadcast(node),
         NodeType::RandomNormal => random_update_output(node),
+        NodeType::RandomNormalLike => random_normal_like_update_output(node),
         NodeType::RandomUniform => random_update_output(node),
         NodeType::Range => range_update_outputs(node),
         NodeType::Reciprocal => same_as_input(node),
@@ -204,6 +205,34 @@ fn random_update_output(node: &mut Node) {
     })
 }
 
+/// Reads & interprets an optional `dtype` attribute
+fn random_normal_like_update_output(node: &mut Node) {
+    let dtype = node
+        .attrs
+        .get("dtype")
+        .map(|val| DataType::from_i32(val.clone().into_i32()).unwrap())
+        .unwrap_or(DataType::FLOAT);
+
+    let elem_type = match dtype {
+        DataType::FLOAT => ElementType::Float32,
+        DataType::FLOAT16 => ElementType::Float16,
+        DataType::DOUBLE => ElementType::Float64,
+        _ => panic!("Tensor with type {dtype:?} not supported for random output"),
+    };
+
+    if let ArgType::Tensor(tensor) = &node.inputs[0].clone().ty {
+        if let Some(shape) = tensor.shape.clone() {
+            node.outputs[0].ty = ArgType::Tensor(TensorType {
+                elem_type,
+                dim: shape.len(),
+                shape: Some(shape),
+            })
+        }
+    } else {
+        panic!("Only tensor input is valid");
+    }
+}
+
 /// Infer the shape of the output tensor of a Conv2d node
 fn linear_update_outputs(node: &mut Node) {
     // Extract the configuration of the linear layer (inputs are known)