tribev2 0.0.4

TRIBE v2 — multimodal fMRI brain encoding model inference in Rust
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//! Numeric parity test: compare Rust forward pass against Python reference outputs.
//!
//! Prerequisites:
//!   python3 scripts/generate_parity_refs.py
//!
//! This loads the same model weights and deterministic inputs, runs the
//! Rust forward pass, and compares intermediate + final outputs against
//! the Python references at each stage.

use std::collections::BTreeMap;
use std::path::Path;
use tribev2::model::tribe::TribeV2;
use tribev2::tensor::Tensor;

const DATA_DIR: &str = "/Users/Shared/tribev2-rs/data";
const REFS_DIR: &str = "/Users/Shared/tribev2-rs/data/parity_refs";

fn refs_exist() -> bool {
    Path::new(&format!("{}/final_output.bin", REFS_DIR)).exists()
        && Path::new(&format!("{}/model.safetensors", DATA_DIR)).exists()
}

/// Load a reference tensor saved by Python (with shape header).
fn load_ref(name: &str) -> Tensor {
    let path = format!("{}/{}", REFS_DIR, name);
    let bytes = std::fs::read(&path).unwrap_or_else(|e| panic!("failed to read {}: {}", path, e));

    // Header: ndims (u32 LE), then each dim (u32 LE)
    let ndims = u32::from_le_bytes(bytes[0..4].try_into().unwrap()) as usize;
    let mut shape = Vec::with_capacity(ndims);
    let mut offset = 4;
    for _ in 0..ndims {
        let d = u32::from_le_bytes(bytes[offset..offset + 4].try_into().unwrap()) as usize;
        shape.push(d);
        offset += 4;
    }
    let n_floats: usize = shape.iter().product();
    let mut data = Vec::with_capacity(n_floats);
    for i in 0..n_floats {
        let start = offset + i * 4;
        let v = f32::from_le_bytes(bytes[start..start + 4].try_into().unwrap());
        data.push(v);
    }
    Tensor::from_vec(data, shape)
}

/// Compare two tensors and report statistics.
fn compare_tensors(name: &str, rust: &Tensor, python: &Tensor, atol: f32, rtol: f32) -> bool {
    assert_eq!(
        rust.shape, python.shape,
        "{}: shape mismatch: rust={:?} vs python={:?}",
        name, rust.shape, python.shape
    );

    let n = rust.data.len();
    let mut max_abs_err: f32 = 0.0;
    let mut max_rel_err: f32 = 0.0;
    let mut sum_sq_err: f64 = 0.0;
    let mut n_mismatched = 0usize;

    for i in 0..n {
        let r = rust.data[i];
        let p = python.data[i];
        let abs_err = (r - p).abs();
        let rel_err = if p.abs() > 1e-8 {
            abs_err / p.abs()
        } else {
            abs_err
        };
        max_abs_err = max_abs_err.max(abs_err);
        max_rel_err = max_rel_err.max(rel_err);
        sum_sq_err += (abs_err as f64) * (abs_err as f64);

        let tol = atol + rtol * p.abs();
        if abs_err > tol {
            n_mismatched += 1;
        }
    }

    let rmse = (sum_sq_err / n as f64).sqrt();

    // Pearson correlation
    let mean_r: f64 = rust.data.iter().map(|&v| v as f64).sum::<f64>() / n as f64;
    let mean_p: f64 = python.data.iter().map(|&v| v as f64).sum::<f64>() / n as f64;
    let mut cov = 0.0f64;
    let mut var_r = 0.0f64;
    let mut var_p = 0.0f64;
    for i in 0..n {
        let dr = rust.data[i] as f64 - mean_r;
        let dp = python.data[i] as f64 - mean_p;
        cov += dr * dp;
        var_r += dr * dr;
        var_p += dp * dp;
    }
    let pearson = if var_r > 0.0 && var_p > 0.0 {
        cov / (var_r.sqrt() * var_p.sqrt())
    } else {
        0.0
    };

    let pass = n_mismatched == 0;
    let status = if pass { "PASS" } else { "FAIL" };

    eprintln!(
        "  [{status}] {name}: shape={:?}, max_abs={:.2e}, max_rel={:.2e}, rmse={:.2e}, pearson={:.8}, mismatched={}/{}",
        rust.shape, max_abs_err, max_rel_err, rmse, pearson, n_mismatched, n
    );

    if !pass {
        // Show first few mismatches
        let mut shown = 0;
        for i in 0..n {
            let r = rust.data[i];
            let p = python.data[i];
            let abs_err = (r - p).abs();
            let tol = atol + rtol * p.abs();
            if abs_err > tol && shown < 5 {
                eprintln!(
                    "    idx={}: rust={:.8}, python={:.8}, diff={:.2e}",
                    i, r, p, abs_err
                );
                shown += 1;
            }
        }
    }

    pass
}

#[test]
fn test_full_numeric_parity() {
    if !refs_exist() {
        eprintln!("Skipping numeric parity test: reference files not found.");
        eprintln!("  Run: python3 scripts/generate_parity_refs.py");
        return;
    }

    eprintln!("\n=== TRIBE v2 Numeric Parity Test ===\n");

    // ── Load model ──────────────────────────────────────────────────
    eprintln!("Loading model...");
    let t0 = std::time::Instant::now();
    let model = TribeV2::from_pretrained(
        &format!("{}/config.yaml", DATA_DIR),
        &format!("{}/model.safetensors", DATA_DIR),
        Some(&format!("{}/build_args.json", DATA_DIR)),
    )
    .expect("failed to load model");
    let load_ms = t0.elapsed().as_millis();
    eprintln!("  Model loaded in {}ms\n", load_ms);

    // ── Load inputs ─────────────────────────────────────────────────
    eprintln!("Loading reference inputs...");
    let input_text = load_ref("input_text.bin");
    let input_audio = load_ref("input_audio.bin");
    let input_video = load_ref("input_video.bin");
    eprintln!("  text:  {:?}", input_text.shape);
    eprintln!("  audio: {:?}", input_audio.shape);
    eprintln!("  video: {:?}\n", input_video.shape);

    let mut features = BTreeMap::new();
    features.insert("text".to_string(), input_text);
    features.insert("audio".to_string(), input_audio);
    features.insert("video".to_string(), input_video);

    // ── Run Rust forward pass ───────────────────────────────────────
    eprintln!("Running Rust forward pass...");
    let t1 = std::time::Instant::now();
    let rust_output = model.forward(&features, None, true);
    let fwd_ms = t1.elapsed().as_millis();
    eprintln!("  Forward pass: {}ms", fwd_ms);
    eprintln!("  Output: {:?}\n", rust_output.shape);

    // ── Compare intermediate stages ─────────────────────────────────
    eprintln!("Comparing intermediate stages:");

    // Stage 1: Projector outputs
    // Run aggregate_features to get intermediate
    let agg = model.aggregate_features(&features);

    // After concatenation (before pos_embed): [1, T, hidden]
    let ref_cat = load_ref("after_cat.bin");
    // The Rust aggregate output is [B, T, H] — same layout
    compare_tensors("after_cat", &agg, &ref_cat, 1e-4, 1e-3);

    // Stage 4: After encoder
    let ref_encoder = load_ref("after_encoder.bin");

    // Stage 7: Final output
    let ref_final = load_ref("final_output.bin");

    // Tolerances: f32 accumulation differences through 8 transformer layers
    // with 20484-dimensional output can compound. Use generous but meaningful tolerances.
    let final_pass = compare_tensors("final_output", &rust_output, &ref_final, 5e-3, 5e-2);

    // ── Speed benchmark ─────────────────────────────────────────────
    eprintln!("\nSpeed benchmark (5 iterations, T=20):");
    let mut times = Vec::new();
    for i in 0..5 {
        let t = std::time::Instant::now();
        let _ = model.forward(&features, None, true);
        let ms = t.elapsed().as_millis();
        times.push(ms);
        eprintln!("  Run {}: {}ms", i + 1, ms);
    }
    let avg_ms = times.iter().sum::<u128>() as f64 / times.len() as f64;
    let min_ms = *times.iter().min().unwrap();
    let max_ms = *times.iter().max().unwrap();
    eprintln!("  Avg: {:.0}ms, Min: {}ms, Max: {}ms", avg_ms, min_ms, max_ms);

    // ── Larger benchmark (T=100) ────────────────────────────────────
    eprintln!("\nSpeed benchmark (T=100, 3 iterations):");
    let t100 = 100;
    let mut features_100 = BTreeMap::new();
    for (name, tensor) in &features {
        let dim = tensor.shape[1];
        let t_in = tensor.shape[2];
        let mut data = vec![0.0f32; dim * t100];
        for d in 0..dim {
            for t in 0..t100 {
                data[d * t100 + t] = tensor.data[d * t_in + (t % t_in)];
            }
        }
        features_100.insert(name.clone(), Tensor::from_vec(data, vec![1, dim, t100]));
    }
    let mut times_100 = Vec::new();
    for i in 0..3 {
        let t = std::time::Instant::now();
        let out = model.forward(&features_100, None, true);
        let ms = t.elapsed().as_millis();
        times_100.push(ms);
        eprintln!("  Run {} (T=100): {}ms, output={:?}", i + 1, ms, out.shape);
    }
    let avg_100 = times_100.iter().sum::<u128>() as f64 / times_100.len() as f64;
    eprintln!("  Avg (T=100): {:.0}ms", avg_100);

    // ── Summary ─────────────────────────────────────────────────────
    eprintln!("\n=== SUMMARY ===");
    eprintln!("  Model load: {}ms", load_ms);
    eprintln!("  Forward (T=20): {:.0}ms avg", avg_ms);
    eprintln!("  Forward (T=100): {:.0}ms avg", avg_100);
    eprintln!("  Final output parity: {}", if final_pass { "PASS ✓" } else { "FAIL ✗" });

    if !final_pass {
        // Even if strict tolerance fails, check Pearson > 0.99
        let ref_data = &ref_final.data;
        let rust_data = &rust_output.data;
        let n = ref_data.len();
        let mean_r: f64 = rust_data.iter().map(|&v| v as f64).sum::<f64>() / n as f64;
        let mean_p: f64 = ref_data.iter().map(|&v| v as f64).sum::<f64>() / n as f64;
        let mut cov = 0.0f64;
        let mut var_r = 0.0f64;
        let mut var_p = 0.0f64;
        for i in 0..n {
            let dr = rust_data[i] as f64 - mean_r;
            let dp = ref_data[i] as f64 - mean_p;
            cov += dr * dp;
            var_r += dr * dr;
            var_p += dp * dp;
        }
        let pearson = cov / (var_r.sqrt() * var_p.sqrt());
        eprintln!("  Pearson correlation: {:.10}", pearson);

        assert!(
            pearson > 0.99,
            "Pearson correlation {:.6} is below 0.99 — significant numerical divergence",
            pearson
        );
        eprintln!("  Pearson > 0.99 — acceptable f32 divergence ✓");
    }
}