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fix: rescore_deploy tolerates old-schema checkpoints (default eval-harness params)

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job 32/33 failed KeyError eval_batch_size: old checkpoints' stored cfg
predates the train_config refactor. Default eval_n_prompts/max_new/
eval_batch_size to the fast preset (eval-harness params, not model-defining;
test split is fixed-size) so historical checkpoints re-score.

Co-Authored-By: Claudypoo <288921227+claudypoo@users.noreply.github.com>
This commit is contained in:
wassname
2026-06-10 03:31:07 +00:00
parent e7ed74956f
commit b334b5f516
1 changed files with 10 additions and 6 deletions
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scripts/rescore_deploy.py
+10 -6
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@@ -48,20 +48,24 @@ def main(run_dir: Positional[Path]) -> None:
# by_mode keys ARE the modes the original deploy eval spanned (present in every json
# version); reproduce the same set so the re-scored knob-off matches the headline.
eval_modes = sorted(prior_eval["by_mode"].keys())
_, problems = load_eval_splits(eval_modes, cfg["eval_n_prompts"])
# Eval-harness params (not model-defining): default to the fast preset so we can re-score
# OLD checkpoints whose stored cfg predates the train_config refactor (eval_n_prompts /
# eval_batch_size were added/renamed). The test split is fixed-size regardless of n_prompts.
eval_n_prompts = cfg.get("eval_n_prompts", 32)
max_new = cfg.get("max_new", 512)
eval_bs = cfg.get("eval_batch_size", 8)
_, problems = load_eval_splits(eval_modes, eval_n_prompts)
gen_cfg_eval = GenerationConfig(
max_new_tokens=cfg["max_new"], do_sample=True,
max_new_tokens=max_new, do_sample=True,
temperature=0.7, top_p=1.0, top_k=20, min_p=0.0, repetition_penalty=1.0,
num_return_sequences=1, pad_token_id=tok.pad_token_id,
)
eval_idxs = list(range(len(problems)))
torch.manual_seed(EVAL_GEN_SEED)
with ablate_quarantine(wrappers): # knob OFF = the deployed model
ev = eval_hack_solve(
model, tok, problems, eval_idxs, gen_cfg_eval, device, cfg["max_new"], cfg["eval_batch_size"])
ev = eval_hack_solve(model, tok, problems, eval_idxs, gen_cfg_eval, device, max_new, eval_bs)
torch.manual_seed(EVAL_GEN_SEED)
ev_on = eval_hack_solve(
model, tok, problems, eval_idxs, gen_cfg_eval, device, cfg["max_new"], cfg["eval_batch_size"])
ev_on = eval_hack_solve(model, tok, problems, eval_idxs, gen_cfg_eval, device, max_new, eval_bs)
out = {
"schema": RUN_SCHEMA,