mirror of
https://github.com/wassname/autoresearch_template.git
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wassname
112 lines
3.1 KiB
Python
112 lines
3.1 KiB
Python
"""
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FROZEN: only edit in meta-mode (META_MODE=1).
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Frozen to prevent p-hacking, seed hacking, and eval-set leakage.
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All experiments must be compared using this exact eval logic.
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Appends one row to results.tsv per run.
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"""
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# FROZEN: only edit in meta-mode (META_MODE=1)
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import csv
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import os
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import subprocess
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from dataclasses import asdict, dataclass
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from datetime import datetime
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from pathlib import Path
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import torch
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import tyro
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from loguru import logger
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# {FILL_IN}: import your model
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# from train import Config, build_model
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EVAL_SEED = 42 # FROZEN: never change
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EVAL_SPLIT = "val" # FROZEN: never change
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RESULTS_FILE = Path(__file__).parent / "results.tsv"
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@dataclass
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class EvalConfig:
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checkpoint: str = "" # path to checkpoint or empty for untrained baseline
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# {FILL_IN}: add any eval-time config here (e.g. batch_size, max_seq_len)
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batch_size: int = 32
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def get_git_info() -> dict:
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def run(cmd):
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return subprocess.check_output(cmd, text=True).strip()
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return {
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"commit": run(["git", "rev-parse", "--short", "HEAD"]),
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"branch": run(["git", "rev-parse", "--abbrev-ref", "HEAD"]),
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"worktree": Path.cwd().name,
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}
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def load_eval_data():
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"""Load fixed evaluation data. FROZEN: do not change split or preprocessing."""
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torch.manual_seed(EVAL_SEED)
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# {FILL_IN}: load your dataset
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# e.g. dataset = load_dataset("openwebtext", split=EVAL_SPLIT)
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raise NotImplementedError("{FILL_IN}: implement load_eval_data()")
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def evaluate(model, data) -> dict:
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"""Run evaluation. FROZEN: do not change metric computation."""
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model.eval()
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torch.manual_seed(EVAL_SEED)
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# {FILL_IN}: compute your metric(s)
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# e.g. return {"val_bpb": compute_bpb(model, data), "val_loss": compute_loss(model, data)}
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raise NotImplementedError("{FILL_IN}: implement evaluate()")
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def append_results(row: dict):
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"""Append one row to results.tsv. Creates file with header if missing."""
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fieldnames = list(row.keys())
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file_exists = RESULTS_FILE.exists()
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with open(RESULTS_FILE, "a", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=fieldnames, delimiter="\t")
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if not file_exists:
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writer.writeheader()
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writer.writerow(row)
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logger.info(f"Results appended to {RESULTS_FILE}")
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def main(cfg: EvalConfig):
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logger.info(f"Eval config: {cfg}")
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git = get_git_info()
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data = load_eval_data()
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# {FILL_IN}: build and load your model
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# from train import Config, build_model
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# model_cfg = Config()
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# model = build_model(model_cfg)
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# if cfg.checkpoint:
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# model.load_state_dict(torch.load(cfg.checkpoint))
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raise NotImplementedError("{FILL_IN}: build and load model")
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metrics = evaluate(model, data)
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row = {
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"date": datetime.now().isoformat(timespec="seconds"),
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"commit": git["commit"],
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"branch": git["branch"],
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"worktree": git["worktree"],
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**metrics,
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# {FILL_IN}: add config fields you want tracked (e.g. model_cfg.n_layers)
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}
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logger.info(f"Metrics: {metrics}")
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append_results(row)
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if __name__ == "__main__":
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tyro.cli(main)
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