8386f0f45a
The duplicate ports neural/jpegai_hyper.rs and neural/jpegai_mans.rs, which the float decode path ran its front half with, are deleted (O10): that path runs the shared driver since the switch. The direct pieces of the exact decoder leave production, test-only (legacy_int): the crate-root mANS port (jpegai_mans), the hyper-scale network (jpegai_synthesis::decode_hyper_scale and its convolution) and the pipeline's skip_mask, rvs_additive, rvs_likely and sigma_index. The calibration tests that read them require legacy_int. jpegai_mans_golden runs on E8 and the scale graph alone; jpegai_e2e (the float chain against the reference PNG) takes its front half from the driver, still checking the dumped RVS additions, masks and residuals; the byte-equality crate drops the neural port and keeps E8 and the direct port. Co-Authored-By: Claude Opus 5.5 <[email protected]>
59 lines
3.3 KiB
Rust
59 lines
3.3 KiB
Rust
//! End-to-end integration: the entropy → dequant spine segment on real `test256.bin`
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//! data. Decode the y-latent symbols with the mANS decoder (`MansDecoder::decode_sgm`
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//! over the real SORP stream), then dequantize by the per-position gain map, and
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//! require BYTE-EXACT (f32) equality to the reference `resi_dq_y` checkpoint. This
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//! proves the mANS output is exactly the dequant input and the dequant arithmetic is
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//! `y_q · gain_map`.
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//!
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//! env: WAI_JPEGAI_MANS_FIXTURE = mans_sorp_fixture.json {dt,bounds,mem,offsets,calls}
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//! WAI_JPEGAI_DEQUANT_FIXTURE = jpegai_dequant_fixture.json {gain_map[f32], resi_dq[f32]}
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#![cfg(all(feature = "neural_int", feature = "legacy_int"))]
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use wai::jpegai_mans::MansDecoder;
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use wai::jpegai_pipeline::dequant;
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#[test]
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fn entropy_to_dequant_byte_exact() {
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let mf = match std::env::var("WAI_JPEGAI_MANS_FIXTURE") {
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Ok(p) if std::path::Path::new(&p).exists() => p,
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_ => { eprintln!("skip: set WAI_JPEGAI_MANS_FIXTURE"); return; }
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};
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let df = match std::env::var("WAI_JPEGAI_DEQUANT_FIXTURE") {
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Ok(p) if std::path::Path::new(&p).exists() => p,
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_ => { eprintln!("skip: set WAI_JPEGAI_DEQUANT_FIXTURE"); return; }
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};
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let m: serde_json::Value = serde_json::from_slice(&std::fs::read(&mf).unwrap()).unwrap();
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let d: serde_json::Value = serde_json::from_slice(&std::fs::read(&df).unwrap()).unwrap();
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// --- entropy: decode the whole y-latent (160 calls × 256) via mANS ---
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let dt: Vec<u32> = m["dt"].as_array().unwrap().iter().map(|x| x.as_u64().unwrap() as u32).collect();
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let bounds: Vec<u8> = m["bounds"].as_array().unwrap().iter().map(|x| x.as_u64().unwrap() as u8).collect();
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let mem: Vec<u8> = m["mem"].as_array().unwrap().iter().map(|x| x.as_u64().unwrap() as u8).collect();
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let offsets: Vec<u32> = m["offsets"].as_array().unwrap().iter().map(|x| x.as_u64().unwrap() as u32).collect();
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let mut dec = MansDecoder::new(&mem, &offsets);
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dec.set_sgm_transitions(&dt, &bounds);
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let mut y_q: Vec<i16> = Vec::new();
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for call in m["calls"].as_array().unwrap() {
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let sigma: Vec<u8> = call["sigma"].as_array().unwrap().iter().map(|x| x.as_u64().unwrap() as u8).collect();
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let mask: Vec<bool> = call["mask"].as_array().unwrap().iter().map(|x| x.as_bool().unwrap()).collect();
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let mut out = vec![0i16; sigma.len()];
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dec.decode_sgm(&sigma, &mut out, &mask);
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y_q.extend_from_slice(&out);
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}
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// --- dequant: resi = y_q · gain_map ---
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let gain_map: Vec<f32> = d["gain_map"].as_array().unwrap().iter().map(|x| x.as_f64().unwrap() as f32).collect();
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let resi_ref: Vec<f32> = d["resi_dq"].as_array().unwrap().iter().map(|x| x.as_f64().unwrap() as f32).collect();
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let resi = dequant(&y_q, &gain_map);
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assert_eq!(resi.len(), resi_ref.len(), "size mismatch");
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let (mut maxd, mut mism) = (0f32, 0usize);
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for i in 0..resi.len() {
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let dlt = (resi[i] - resi_ref[i]).abs();
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// exact-in-f32: allow only 1-ULP-scale rounding vs the stored f64->f32 golden
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if dlt > 1e-4 { mism += 1; }
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maxd = maxd.max(dlt);
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}
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eprintln!("entropy→dequant vs reference resi_dq_y: {} vals, max|Δ|={maxd:.2e}, mismatched(>1e-4)={mism}", resi.len());
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assert_eq!(mism, 0, "entropy→dequant must be byte-exact (f32); {mism} mismatches");
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}
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