foxhunt/crates/ml/tests/scatter_add_gradient_test.rs

use candle_core::{Device, Tensor};
use candle_nn::{VarBuilder, VarMap};
use ml::MLError;

#[test]
fn test_scatter_add_gradient_flow() -> Result<(), MLError> {
    println!("\n=== Testing scatter_add gradient flow ===\n");

    let device = Device::cuda_if_available(0)?;

    // Create learnable source values
    let varmap = VarMap::new();
    let vb = VarBuilder::from_varmap(&varmap, candle_core::DType::F32, &device);

    let source = vb.get((2, 3), "source")?;
    println!("Source values: {:?}", source.to_vec2::<f32>()?);

    // Create indices (fixed, not learnable)
    let indices = Tensor::new(&[[0i64, 1, 2], [2, 1, 0]], &device)?;
    println!("Indices: {:?}", indices.to_vec2::<i64>()?);

    // Create base tensor
    let base = Tensor::zeros((2, 5), candle_core::DType::F32, &device)?;

    // Scatter add
    let result = base.scatter_add(&indices, &source, 1)?;
    println!("Result: {:?}", result.to_vec2::<f32>()?);

    // Compute loss
    let loss = result.sum_all()?;
    println!("Loss: {:.6}", loss.to_scalar::<f32>()?);

    // Backward
    let grads = loss.backward()?;

    // Check gradients
    let all_vars = varmap.all_vars();
    if let Some(grad) = grads.get(&all_vars[0]) {
        println!("Gradients: {:?}", grad.to_vec2::<f32>()?);
        let grad_norm = grad.sqr()?.sum_all()?.to_scalar::<f32>()?.sqrt();
        println!("Gradient norm: {:.6}", grad_norm);

        assert!(
            grad_norm > 1e-6,
            "scatter_add breaks gradient flow! Got norm={:.9}",
            grad_norm
        );

        println!("\n✅ SUCCESS: scatter_add preserves gradient flow!\n");
    } else {
        panic!("No gradients computed!");
    }

    Ok(())
}