riemann-flow-gnn/examples/toy_pipeline.rs

use std::{collections::BTreeMap, marker::PhantomData, path::Path};
use std::fs;

use burn::{backend::Autodiff, tensor::backend::AutodiffBackend};
use riemann_flow_gnn::{
    api::{OneSampleToyBatch, OneSampleToyPipeline},
    conformer::LigandConformer,
    geometry::PoseState,
    graph::{Atom, Bond, LigandGraph, TorsionEdge},
    viz::{export_simulation_video, plot_static_png},
};
use serde::Deserialize;
use riemann_flow_gnn::api::burn_training::{
    run_burn_training, BurnToyModel, BurnTrainConfig,
};
use safetensors::{
    serialize_to_file,
    tensor::{Dtype, TensorView},
};

type ExampleBackend = Autodiff<burn::backend::CudaJit>;

#[derive(Debug, Deserialize)]
struct ToyAtom {
    index: usize,
    atomic_number: u8,
    formal_charge: i8,
    is_aromatic: bool,
}

#[derive(Debug, Deserialize)]
struct ToyBond {
    index: usize,
    src: usize,
    dst: usize,
    bond_order: u8,
    is_aromatic: bool,
    is_rotatable: bool,
}

#[derive(Debug, Deserialize)]
struct ToyReferenceData {
    atoms: Vec<ToyAtom>,
    bonds: Vec<ToyBond>,
    coords_angstrom: Vec<[f32; 3]>,
    torsion_edges: Vec<TorsionEdgeJson>,
}

#[derive(Debug, Deserialize)]
struct TorsionEdgeJson {
    bond_index: usize,
    atom_left: usize,
    atom_right: usize,
    rotating_side: Vec<usize>,
}

fn load_toy_reference(path: &str) -> Result<(LigandGraph, LigandConformer), Box<dyn std::error::Error>> {
    let raw = fs::read_to_string(path)?;
    let parsed: ToyReferenceData = serde_json::from_str(&raw)?;

    let atoms = parsed
        .atoms
        .into_iter()
        .map(|a| Atom {
            index: a.index,
            atomic_number: a.atomic_number,
            formal_charge: a.formal_charge,
            is_aromatic: a.is_aromatic,
        })
        .collect::<Vec<_>>();
    let bonds = parsed
        .bonds
        .into_iter()
        .map(|b| Bond {
            index: b.index,
            src: b.src,
            dst: b.dst,
            bond_order: b.bond_order,
            is_aromatic: b.is_aromatic,
            is_rotatable: b.is_rotatable,
        })
        .collect::<Vec<_>>();
    let torsion_edges = parsed
        .torsion_edges
        .into_iter()
        .map(|t| TorsionEdge {
            bond_index: t.bond_index,
            atom_left: t.atom_left,
            atom_right: t.atom_right,
            rotating_side: t.rotating_side,
        })
        .collect::<Vec<_>>();
    let graph = LigandGraph::new(atoms, bonds, torsion_edges);
    let reference = LigandConformer::new(graph.clone(), parsed.coords_angstrom)?;
    Ok((graph, reference))
}

struct ExampleBurnModel<B: AutodiffBackend> {
    _backend: PhantomData<B>,
}

impl<B: AutodiffBackend> ExampleBurnModel<B> {
    fn forward_template(&self, _graph: &riemann_flow_gnn::graph::GraphFeatures) -> Result<(), String> {
        // TODO(user): Burn tensor 입력(GraphTensors, PoseTensors) 기반 forward 구현
        Ok(())
    }

    fn loss_template(&self) -> Result<f64, String> {
        // TODO(user): translation/rotation/torsion loss 조합 구현
        Ok(1.0)
    }

    fn optimizer_step_template(&mut self, _lr: f64) -> Result<(), String> {
        // TODO(user): Burn optimizer step 구현
        Ok(())
    }
}

impl<B: AutodiffBackend> BurnToyModel<B> for ExampleBurnModel<B> {
    fn name(&self) -> &'static str {
        "burn-toy-model"
    }

    fn new(_device: &B::Device) -> Self {
        Self {
            _backend: PhantomData,
        }
    }

    fn initialize(
        &mut self,
        _graph: &riemann_flow_gnn::graph::GraphFeatures,
        _device: &B::Device,
    ) -> Result<(), String> {
        // TODO(user): Burn 모델 파라미터 초기화
        Ok(())
    }

    fn train_step(
        &mut self,
        graph: &riemann_flow_gnn::graph::GraphFeatures,
        epoch: usize,
        lr: f64,
    ) -> Result<f64, String> {
        self.forward_template(graph)?;
        let base_loss = self.loss_template()?;
        self.optimizer_step_template(lr)?;
        Ok(base_loss / (epoch as f64))
    }

    fn save_model_safetensors(&self, path: &Path) -> Result<(), String> {
        // TODO(user): 실제 model state tensor들을 safetensors로 저장
        let value_bytes = 1.0f32.to_le_bytes();
        let value_view =
            TensorView::new(Dtype::F32, vec![1], &value_bytes).map_err(|e| e.to_string())?;
        let tensors = BTreeMap::from([("placeholder_weight".to_string(), value_view)]);
        serialize_to_file(tensors, &None, path).map_err(|e| e.to_string())?;
        Ok(())
    }

    fn load_model_safetensors(_device: &B::Device, _path: &Path) -> Result<Self, String> {
        // TODO(user): safetensors에서 model state 복원
        Ok(Self {
            _backend: PhantomData,
        })
    }
}

fn main() -> Result<(), Box<dyn std::error::Error>> {
    let (graph, reference) = load_toy_reference("data/toy/aspirin_reference.json")?;

    let pose_start = PoseState {
        translation: [0.0, 0.0, 0.0],
        rotation_quat_xyzw: [0.0, 0.0, 0.0, 1.0],
        torsions: vec![0.0],
    };
    let pose_target = PoseState {
        translation: [1.0, 1.5, 0.5],
        rotation_quat_xyzw: [0.0, 0.0, 0.38268343, 0.9238795],
        torsions: vec![1.2],
    };

    let pipeline = OneSampleToyPipeline::new(OneSampleToyBatch {
        graph,
        reference: reference.clone(),
        pose_start,
        pose_target,
    });
    let graph_features = pipeline.graph_contract();

    let history = {
        let device = Default::default();
        let mut model = ExampleBurnModel::<ExampleBackend>::new(&device);
        let train_config = BurnTrainConfig {
            epochs: 20,
            learning_rate: 1e-3,
            checkpoint_every: 5,
            checkpoint_dir: "outputs/checkpoints".to_string(),
        };
        run_burn_training::<ExampleBackend, _>(
            &mut model,
            &graph_features,
            &device,
            &train_config,
        )?
    };

    // 3) simulation video export
    let trajectory = export_simulation_video(
        &pipeline.batch.reference,
        &pipeline.batch.pose_start,
        &pipeline.batch.pose_target,
        48,
        "outputs",
        24,
    )?;
    plot_static_png(&trajectory[0], "outputs/static_start.png")?;
    plot_static_png(trajectory.last().ok_or("empty trajectory")?, "outputs/static_end.png")?;
    println!(
        "contract: nodes={}, edges={}, torsions={}, node_dim={}, edge_dim={}",
        graph_features.num_nodes,
        graph_features.num_edges,
        graph_features.num_torsion_edges,
        graph_features.spec.node_feat_dim,
        graph_features.spec.edge_feat_dim
    );
    println!(
        "burn training finished: epochs={}, last_loss={:.6}",
        history.len(),
        history.last().map(|m| m.loss).unwrap_or(0.0)
    );
    println!("saved checkpoints: outputs/checkpoints/*.safetensors");
    println!("saved video: outputs/simulation.mp4");
    println!("target reference: data/toy/aspirin_reference.json");
    Ok(())
}