e6453a9566
Gate 7 made these seven tests require `legacy_int` in Cargo.toml, but their files were still gated on `neural_int` or `neural` alone, which `check-tests-in-ci.py` reports: a file's `#![cfg]` must name what its `[[test]]` requires. Each file is now gated on `all(..., feature = "legacy_int")`, as `jpegai_ops.rs` is. Three test comments named the design's appendix and a plan item. They now name the text's sections (§11's `role_profiles`, §13.6.2's table). Co-Authored-By: Claude Opus 5.5 <[email protected]>
63 lines
2.8 KiB
Rust
63 lines
2.8 KiB
Rust
//! Calibration for [`wai::jpegai_pipeline::reconstruct_yhat`] — the SGMM
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//! autoregressive spine — on REAL `test256.bin` data: rebuild the synthesis-input
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//! latent `y_hat` from the reference's dequantized residual (`resi_dq_y`) and hyper
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//! params (`psi_y`), and compare against the reference `yhat_y`.
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//!
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//! This closes the last structural gap in the decode chain: entropy → dequant →
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//! **reconstruct** → synthesis. The float reference loop reproduces `yhat_y` to
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//! ~139 dB (float round-off); our Q16 integer MCM stages carry the 16-bit PTQ floor
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//! of the MCM nets themselves (~61–74 dB per stage), so we assert well above that.
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//!
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//! env: WAI_JPEGAI_RECON_FIXTURE = recon_fixture.json {resi_dq_y, psi_y, yhat_y, c, h2, w2}
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//! WAI_JPEGAI_MCM_WEIGHTS_DIR = dir with mcm_stage0..3_y.json
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#![cfg(all(feature = "neural_int", feature = "legacy_int"))]
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use wai::jpegai_pipeline::reconstruct_yhat;
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use wai::jpegai_synthesis::Weights;
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fn q16(f: &[f64]) -> Vec<i64> { f.iter().map(|&x| (x * 65536.0).round() as i64).collect() }
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#[test]
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fn reconstruct_yhat_on_real_reference_data() {
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let fx = match std::env::var("WAI_JPEGAI_RECON_FIXTURE") {
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Ok(p) if std::path::Path::new(&p).exists() => p,
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_ => { eprintln!("skip: set WAI_JPEGAI_RECON_FIXTURE"); return; }
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};
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let wdir = match std::env::var("WAI_JPEGAI_MCM_WEIGHTS_DIR") {
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Ok(p) if std::path::Path::new(&p).exists() => p,
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_ => { eprintln!("skip: set WAI_JPEGAI_MCM_WEIGHTS_DIR"); return; }
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};
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let v: serde_json::Value = serde_json::from_slice(&std::fs::read(&fx).unwrap()).unwrap();
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let f64v = |k: &str| v[k].as_array().unwrap().iter().map(|x| x.as_f64().unwrap()).collect::<Vec<f64>>();
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let (c, h2, w2) = (
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v["c"].as_u64().unwrap() as usize,
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v["h2"].as_u64().unwrap() as usize,
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v["w2"].as_u64().unwrap() as usize,
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);
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let resi = q16(&f64v("resi_dq_y"));
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let psi = q16(&f64v("psi_y"));
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let yhat_ref = f64v("yhat_y");
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let load = |i: usize| -> Weights {
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let raw = std::fs::read(format!("{wdir}/mcm_stage{i}_y.json")).unwrap();
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Weights::from_json(&serde_json::from_slice(&raw).unwrap()).unwrap()
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};
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let (w0, w1, w2w, w3) = (load(0), load(1), load(2), load(3));
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let got = reconstruct_yhat(&resi, &psi, c, h2, w2, [&w0, &w1, &w2w, &w3]);
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assert_eq!(got.len(), yhat_ref.len(), "y_hat size mismatch");
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let (mut maxd, mut se, mut sig) = (0f64, 0f64, 0f64);
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for i in 0..got.len() {
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let g = got[i] as f64 / 65536.0;
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let r = yhat_ref[i];
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let d = (g - r).abs();
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maxd = maxd.max(d);
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se += d * d;
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sig += r * r;
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}
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let snr = 10.0 * (sig / se).log10();
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eprintln!("reconstruct_yhat on real data: max|Δ| = {maxd:.5} SNR = {snr:.2} dB");
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assert!(snr > 45.0, "SGMM reconstruction regressed (SNR {snr:.2} dB)");
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}
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