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

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ZacharyZcR
2026-07-15 14:16:36 +08:00
parent a8895f2d84
commit e7cb7df501
3 changed files with 21 additions and 142 deletions
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c/tools/expert_atlas.py
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#!/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
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@@ -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
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@@ -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()