| |
| """Parse sbob-cmp-q-* job logs into quant_matrix.{json,md}. |
| |
| Each eval_ensemble.py run prints a final table: |
| val set examples loss ppl |
| spongebob 1434 2.0123 7.48 |
| ... |
| Rows = 11 val sets; we collect them per config (base / ensemble / 10 per-char). |
| """ |
| import json, re, subprocess, sys, os |
|
|
| NS = "sbob" |
| SLUGS = ["spongebob", "patrick", "squidward", "mr_krabs", "plankton", |
| "sandy", "mrs_puff", "karen", "gary", "pearl", "_ensemble"] |
| |
| CONFIGS = {"base": "sbob-cmp-q-base", "ensemble": "sbob-cmp-q-ensemble"} |
| for s in SLUGS[:-1]: |
| CONFIGS[s] = "sbob-cmp-q-" + s.replace("_", "-") |
|
|
| ROW_RE = re.compile(r"^(_?[a-z_]+)\s+(\d+)\s+([0-9.]+)\s+([0-9.]+)\s*$") |
|
|
| def logs(job): |
| out = subprocess.run( |
| ["kubectl", "-n", NS, "logs", f"job/{job}", "--tail=200"], |
| capture_output=True, text=True) |
| return out.stdout |
|
|
| def parse(text): |
| rows = {} |
| for ln in text.splitlines(): |
| m = ROW_RE.match(ln.strip()) |
| if m and m.group(1) in SLUGS: |
| rows[m.group(1)] = {"examples": int(m.group(2)), |
| "loss": float(m.group(3)), |
| "ppl": float(m.group(4))} |
| return rows |
|
|
| def main(): |
| matrix, missing = {}, [] |
| for cfg, job in CONFIGS.items(): |
| rows = parse(logs(job)) |
| if len(rows) != len(SLUGS): |
| missing.append((cfg, sorted(set(SLUGS) - set(rows)))) |
| matrix[cfg] = rows |
| out_dir = os.path.dirname(os.path.abspath(__file__)) |
| with open(os.path.join(out_dir, "quant_matrix.json"), "w") as f: |
| json.dump(matrix, f, indent=2) |
|
|
| cfg_order = ["base", "ensemble"] + SLUGS[:-1] |
| |
| lines = ["# Quant matrix — masked val loss (gemma-4-31B base vs LoRAs)", |
| "", |
| "Configs: `base` = no adapter; `ensemble` = all-speaker r=64 LoRA; " |
| "others = dedicated per-character r=32 LoRAs. " |
| "Each column is one 1-GPU job evaluating all 11 val sets " |
| "(token-weighted mean masked NLL; lower is better). 2026-06-10.", |
| "", |
| "| val set \\ config | " + " | ".join(cfg_order) + " |", |
| "|---|" + "---|" * len(cfg_order)] |
| flags = [] |
| for slug in SLUGS: |
| vals = {c: matrix.get(c, {}).get(slug, {}).get("loss") for c in cfg_order} |
| present = {c: v for c, v in vals.items() if v is not None} |
| best = min(present.values()) if present else None |
| cells = [] |
| for c in cfg_order: |
| v = vals[c] |
| if v is None: |
| cells.append("—") |
| continue |
| if v > 5 or v < 0.5: |
| flags.append(f"{c} on {slug}: loss {v}") |
| cells.append(f"**{v:.4f}**" if v == best else f"{v:.4f}") |
| n = matrix.get("base", {}).get(slug, {}).get("examples", "?") |
| lines.append(f"| {slug} (n={n}) | " + " | ".join(cells) + " |") |
|
|
| |
| deltas = [] |
| for c in cfg_order: |
| ds = [matrix[c][s]["loss"] - matrix["base"][s]["loss"] |
| for s in SLUGS if s in matrix.get(c, {}) and s in matrix.get("base", {})] |
| deltas.append(f"{sum(ds)/len(ds):+.4f}" if ds else "—") |
| lines.append("| **mean Δ vs base (11 sets)** | " + " | ".join(deltas) + " |") |
|
|
| |
| lines += ["", "## Ensemble vs dedicated, on each character's own val set", "", |
| "| val set | base | dedicated | ensemble | ens Δ vs base | ens Δ vs dedicated |", |
| "|---|---|---|---|---|---|"] |
| for slug in SLUGS[:-1]: |
| b = matrix.get("base", {}).get(slug, {}).get("loss") |
| d = matrix.get(slug, {}).get(slug, {}).get("loss") |
| e = matrix.get("ensemble", {}).get(slug, {}).get("loss") |
| if None in (b, d, e): |
| lines.append(f"| {slug} | — | — | — | — | — |") |
| continue |
| lines.append(f"| {slug} | {b:.4f} | {d:.4f} | {e:.4f} | " |
| f"{e-b:+.4f} | {e-d:+.4f} |") |
| b = matrix.get("base", {}).get("_ensemble", {}).get("loss") |
| e = matrix.get("ensemble", {}).get("_ensemble", {}).get("loss") |
| if b is not None and e is not None: |
| lines.append(f"| _ensemble (own set) | {b:.4f} | — | {e:.4f} | {e-b:+.4f} | — |") |
|
|
| |
| lines += ["", "## Sanity checks", ""] |
| for slug, known in [("squidward", 1.935), ("spongebob", 1.882)]: |
| v = matrix.get("ensemble", {}).get(slug, {}).get("loss") |
| if v is None: |
| lines.append(f"- ensemble on {slug}: MISSING (expected ~{known})") |
| else: |
| ok = abs(v - known) < 0.05 |
| lines.append(f"- ensemble on {slug}: {v:.4f} vs known {known} -> " |
| + ("OK" if ok else "MISMATCH")) |
| lines.append("- degenerate cells (loss >5 or <0.5): " |
| + ("; ".join(flags) if flags else "none")) |
| if missing: |
| lines.append("- incomplete configs: " + "; ".join( |
| f"{c} missing {m}" for c, m in missing)) |
|
|
| with open(os.path.join(out_dir, "quant_matrix.md"), "w") as f: |
| f.write("\n".join(lines) + "\n") |
| print(json.dumps({"missing": missing, "flags": flags}, indent=2)) |
|
|
| if __name__ == "__main__": |
| main() |
|
|
