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name: burn-contrib description: This skill should be used when the user asks about "contributing to Burn", "adding an operation", "burn internals", "burn-tensor", "burn-autodiff", "ONNX converter", "NodeProcessor", or Burn framework development. version: 0.1.0

Burn Contribution

Knowledge for contributing to the Burn framework itself.

Project Architecture

Burn is organized into crates:

Adding a New Operation

Step 1: burn-tensor

Define the operation API:

Copy// burn-tensor/src/tensor/api/float.rs
impl<B: Backend, const D: usize> Tensor<B, D> {
    pub fn my_op(self, other: Self) -> Self {
        Tensor::new(B::float_my_op(self.primitive, other.primitive))
    }
}

Add backend trait method:

Copy// burn-tensor/src/tensor/backend/base.rs
pub trait Backend {
    fn float_my_op(lhs: FloatTensor<Self>, rhs: FloatTensor<Self>) -> FloatTensor<Self>;
}

Step 2: burn-autodiff

Implement gradient computation:

Copy// burn-autodiff/src/ops/tensor.rs
impl<B: Backend> AutodiffBackend for Autodiff<B> {
    fn float_my_op(lhs: FloatTensor<Self>, rhs: FloatTensor<Self>) -> FloatTensor<Self> {
        // Track operation for backward pass
        // Implement gradient formulas
    }
}

Step 3: Backend Implementations

Implement for each backend (NdArray, WGPU, etc.):

Copy// burn-ndarray/src/ops/tensor.rs
impl Backend for NdArray {
    fn float_my_op(lhs: NdArrayTensor<f32>, rhs: NdArrayTensor<f32>) -> NdArrayTensor<f32> {
        // Actual computation
    }
}

ONNX Converter

Adding support for ONNX operators:

NodeProcessor Trait

Copyimpl NodeProcessor for MyOpProcessor {
    fn process(&self, node: &Node, graph: &mut Graph) -> Result<()> {
        // Parse ONNX node attributes
        // Generate Burn operation
    }
}

Registration

Register processor in operator registry:

Copyregistry.register("MyOp", Box::new(MyOpProcessor::new()));

Testing

All new operations need tests:

Copy#[test]
fn test_my_op() {
    let tensor1 = TestTensor::from([1.0, 2.0]);
    let tensor2 = TestTensor::from([3.0, 4.0]);
    let result = tensor1.my_op(tensor2);
    result.into_data().assert_approx_eq(&[...], 4);
}

Additional Resources

Consult references/topic-map-contrib.md for:

• Detailed crate dependency graph
• Autodiff implementation patterns
• Backend fusion/JIT integration
• PR submission guidelines