Merge pull request #245 from ZacharyZcR/tools/atlas-unify

tools: one expert atlas, not two — retire expert_atlas.py, analyze.py gains --web output for the dashboard

c/tools/expert_atlas.py

@@ -1,141 +0,0 @@
-#!/usr/bin/env python3
-"""Expert Atlas (#175): measure per-expert topic affinity by diffing .coli_usage
-across themed probe batches, served through a running colibri API server.
-
-Protocol per category: snapshot .coli_usage -> send probes -> snapshot again;
-the delta is that category's expert-activation spectrum. One engine load total.
-
-Output: experts.json — for every (layer, expert): counts per category,
-normalized affinity, entropy, and a "specialist" label when one topic dominates.
-
-Usage (server already running with the model):
-    python3 tools/expert_atlas.py --api http://127.0.0.1:8000 \
-        --usage /path/to/model/.coli_usage --out experts.json --ngen 64
-"""
-import argparse, json, math, time, urllib.request
-
-PROBES = {
-    "code": [
-        "Write a Python function that parses a CSV file and returns a dict keyed by the first column.",
-        "Explain the difference between a mutex and a semaphore, with a C example.",
-        "Refactor this into idiomatic Rust: for i in range(len(xs)): total += xs[i] * 2",
-    ],
-    "math": [
-        "Prove that the square root of 2 is irrational.",
-        "Compute the derivative of x^3 * ln(x) and explain each step.",
-        "A fair die is rolled 4 times. What is the probability of at least one six?",
-    ],
-    "chinese": [
-        "请用中文解释一下什么是光合作用,以及它对地球生态系统的重要性。",
-        "把这句话翻译成中文并解释语法:The early bird catches the worm.",
-        "写一段关于秋天的短文,一百字左右。",
-    ],
-    "english_prose": [
-        "Write a vivid paragraph describing an old lighthouse keeper watching a storm arrive.",
-        "Summarize the plot of Romeo and Juliet in three sentences.",
-        "Continue this story: The last train left the station, and Maria realized her mistake.",
-    ],
-    "science": [
-        "Explain how mRNA vaccines work at the cellular level.",
-        "Why is the sky blue during the day but red at sunset?",
-        "Describe the life cycle of a massive star, from formation to supernova.",
-    ],
-    "law": [
-        "Explain the difference between a patent, a trademark, and a copyright.",
-        "What are the key elements required to form a legally binding contract?",
-        "Summarize what 'due process' means in constitutional law.",
-    ],
-    "poetry": [
-        "Write a short poem about a hummingbird in the style of Emily Dickinson.",
-        "Compose a haiku about winter rain, then explain its imagery.",
-        "Write four rhyming lines about the sea at night.",
-    ],
-    "structured": [
-        'Convert to JSON: name Alice, age 30, hobbies reading and chess, address 5 Oak St.',
-        "Write a SQL query returning the top 5 customers by total order value, with the schema you assume.",
-        "Write a regex that matches ISO-8601 dates and explain each part.",
-    ],
-    "translation": [
-        "Translate into French, German and Spanish: 'Knowledge is the only treasure that grows when shared.'",
-        "Translate this Italian sentence to English and comment on nuance: 'In bocca al lupo per domani.'",
-        "Translate into Japanese: 'The meeting has been moved to next Tuesday afternoon.'",
-    ],
-    "casual": [
-        "Hey! Any tips for staying awake during boring afternoon meetings?",
-        "What should I cook tonight? I have eggs, rice, tomatoes and some cheese.",
-        "My friend is always late. How do I tell them it bothers me without being rude?",
-    ],
-}
-
-
-def read_usage(path):
-    counts = {}
-    try:
-        with open(path) as f:
-            for line in f:
-                p = line.split()
-                if len(p) == 3:
-                    counts[(int(p[0]), int(p[1]))] = int(p[2])
-    except FileNotFoundError:
-        pass
-    return counts
-
-
-def diff(after, before):
-    return {k: v - before.get(k, 0) for k, v in after.items() if v - before.get(k, 0) > 0}
-
-
-def chat(api, prompt, ngen):
-    body = json.dumps({"model": "glm-5.2-colibri", "stream": False, "max_tokens": ngen,
-                       "messages": [{"role": "user", "content": prompt}]}).encode()
-    req = urllib.request.Request(f"{api}/v1/chat/completions", data=body,
-                                 headers={"Content-Type": "application/json"})
-    with urllib.request.urlopen(req, timeout=600) as r:
-        json.load(r)
-
-
-def main():
-    ap = argparse.ArgumentParser()
-    ap.add_argument("--api", default="http://127.0.0.1:8000")
-    ap.add_argument("--usage", required=True, help="path to the model's .coli_usage")
-    ap.add_argument("--out", default="experts.json")
-    ap.add_argument("--ngen", type=int, default=64)
-    a = ap.parse_args()
-
-    spectra = {}
-    for cat, prompts in PROBES.items():
-        before = read_usage(a.usage)
-        t0 = time.time()
-        for p in prompts:
-            chat(a.api, p, a.ngen)
-        time.sleep(2)                      # let the engine flush .coli_usage
-        spectra[cat] = diff(read_usage(a.usage), before)
-        total = sum(spectra[cat].values())
-        print(f"[{cat}] {len(spectra[cat])} experts touched, {total} selections, {time.time()-t0:.0f}s", flush=True)
-
-    cats = list(PROBES.keys())
-    experts = {}
-    for cat, spec in spectra.items():
-        for k, v in spec.items():
-            experts.setdefault(k, {c: 0 for c in cats})[cat] = v
-
-    atlas = {}
-    for (layer, eid), counts in experts.items():
-        total = sum(counts.values())
-        if total < 8:
-            continue                       # too few observations to characterise
-        aff = {c: v / total for c, v in counts.items() if v}
-        ent = -sum(p * math.log2(p) for p in aff.values())
-        top = max(aff, key=aff.get)
-        label = f"specialist: {top}" if aff[top] >= 0.45 and ent < 2.2 else "generalist"
-        atlas[f"{layer}:{eid}"] = {"counts": counts, "affinity": {c: round(p, 3) for c, p in aff.items()},
-                                   "entropy": round(ent, 2), "top": top, "label": label}
-
-    spec_n = sum(1 for v in atlas.values() if v["label"].startswith("specialist"))
-    with open(a.out, "w") as f:
-        json.dump({"categories": cats, "ngen": a.ngen, "experts": atlas}, f)
-    print(f"\natlas: {len(atlas)} experts characterised, {spec_n} specialists -> {a.out}")
-
-
-if __name__ == "__main__":
-    main()

c/tools/expert_atlas/README.md

@@ -7,10 +7,16 @@ histogram, and turns them into a per-expert topic-affinity vector.
 cd c
 export COLI_MODEL=/path/to/glm52_i4
 ./tools/expert_atlas/sweep.sh                             # 30 probes (10 topics x 3 prompts)
-python3 tools/expert_atlas/analyze.py  --stats atlas_out/stats --out atlas_out/experts.json
+python3 tools/expert_atlas/analyze.py  --stats atlas_out/stats --out atlas_out/experts.json \
+        --web web/dist/experts.json                       # optional: feed the web dashboard Atlas
 python3 tools/expert_atlas/validate.py atlas_out/stats 200 # leave-one-prompt-out check
 ```
 
+`--web` writes the same atlas in the shape the web dashboard consumes (the Atlas galaxy and the
+Brain hover tooltips): keyed `"layer:expert"` with `affinity`/`entropy`/`top`/`label`. It replaces
+the retired `tools/expert_atlas.py`, whose API-driven probing ran through a live server and was
+exposed to exactly the traps above (server-side `--topp`, speculative drafts, shared `.coli_usage`).
+
 ## Read this before you trust any atlas
 
 Four things silently corrupt this measurement. The sweep script controls all of them; if you

c/tools/expert_atlas/analyze.py

@@ -29,6 +29,7 @@ def main():
     ap.add_argument("--min-count", type=int, default=30)
     ap.add_argument("--min-runs", type=int, default=2, help="must fire in >= this many of the top category's runs")
     ap.add_argument("--out", default="experts.json")
+    ap.add_argument("--web", default="", help="also write the web-dashboard experts.json (Atlas/Brain hover)")
     a = ap.parse_args()
 
     # run[(cat,idx)][(layer,expert)] = count ; run_tot[(cat,idx)] = total
@@ -121,6 +122,19 @@ def main():
     json.dump({"categories": cats, "experts": atlas}, open(a.out, "w"), indent=1)
     print(f"wrote {a.out}")
 
+    if a.web:
+        # Same atlas, keyed "layer:expert" with per-expert affinity/entropy/top/label —
+        # the shape the web dashboard consumes (Atlas galaxy, Brain hover).
+        web = {}
+        for r in atlas:
+            aff = {c: v for c, v in r["p"].items() if v > 0}
+            H = -sum(v * math.log2(v) for v in aff.values())
+            web[f"{r['layer']}:{r['expert']}"] = {
+                "affinity": aff, "entropy": round(H, 2), "top": r["top_topic"],
+                "label": f"specialist: {r['top_topic']}" if r["spec"] >= 0.5 else "generalist"}
+        json.dump({"categories": cats, "experts": web}, open(a.web, "w"))
+        print(f"wrote {a.web} (dashboard format, {len(web):,} experts)")
+
 
 if __name__ == "__main__":
     main()