Download train/expert_coverage.py from elyanlabs/elya-nes: direct link, hf CLI and curl.
- Browser
- Download file 1.54 kB
-
https://huggingface.co/elyanlabs/elya-nes/resolve/main/train/expert_coverage.py
- Command line
-
hf download hf://elyanlabs/elya-nes/train/expert_coverage.py
-
curl -L -o expert_coverage.py https://huggingface.co/elyanlabs/elya-nes/resolve/main/train/expert_coverage.py
1.54 kB
| #!/usr/bin/env python3 | |
| """Does the exactness survey actually exercise every expert? | |
| A 64-seed survey that never routes to expert 7 does not test expert 7's header | |
| table, its bank numbers or its copy of the lookup tables - and it would still | |
| report 1,216/1,216. This counts, from the host reference, how often each | |
| expert is routed over exactly the token trajectories the survey runs. | |
| train/expert_coverage.py runs/<arm>.npz [nseeds] | |
| """ | |
| import collections | |
| import os | |
| import sys | |
| HERE = os.path.dirname(os.path.abspath(__file__)) | |
| sys.path.insert(0, os.path.join(HERE, "..", "host")) | |
| import ref | |
| def main(): | |
| npz = sys.argv[1] | |
| n = int(sys.argv[2]) if len(sys.argv) > 2 else 64 | |
| m = ref.Model.from_npz(npz) | |
| ne = max(m.nexp, m.nexp_head) | |
| cnt = collections.Counter() | |
| per_seed = [] | |
| for s in range(n): | |
| toks, _ = ref.generate(m, s, ref.T - 1) | |
| es = [m.route[t] for t in toks[:ref.T]] | |
| cnt.update(es) | |
| per_seed.append(len(set(es))) | |
| tot = sum(cnt.values()) | |
| print("%s: %d seeds x %d positions = %d routed token-positions" | |
| % (npz, n, ref.T, tot)) | |
| for e in range(ne): | |
| print(" expert %-2d %6d %5.1f%%" % (e, cnt[e], 100.0 * cnt[e] / tot)) | |
| never = [e for e in range(ne) if cnt[e] == 0] | |
| print("experts never routed: %s" % (never or "none")) | |
| print("distinct experts per seed: min %d max %d mean %.2f" | |
| % (min(per_seed), max(per_seed), sum(per_seed) / float(n))) | |
| return 1 if never else 0 | |
| if __name__ == "__main__": | |
| sys.exit(main()) | |