src/dropout_decay/experiments/artifacts.py

"""
Derived from Andrej Karpathy's nanochat project.

MIT License

Copyright (c) 2025 Andrej Karpathy

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
"""

from __future__ import annotations

import argparse
import csv
from collections import defaultdict
import json
from pathlib import Path
import statistics

from dropout_decay.specs import DropoutCondition, ModelSpec


SUMMARY_FIELDS = [
    "run_mode",
    "condition",
    "condition_kind",
    "stage",
    "token_limit",
    "model_name",
    "n_layer",
    "n_head",
    "n_embd",
    "parameters",
    "dropout_initial",
    "dropout_final",
    "dropout_schedule",
    "n",
    "mean_train_eval_loss",
    "std_train_eval_loss",
    "mean_val_eval_loss",
    "std_val_eval_loss",
    "mean_generalization_gap",
    "std_generalization_gap",
]
SELECTION_FIELDS = [
    "run_mode",
    "token_limit",
    "model_name",
    "n_layer",
    "n_head",
    "n_embd",
    "parameters",
    "n",
    "best_dropout",
    "best_val_loss",
    "best_val_std",
    "plateau_start_dropout",
    "plateau_end_dropout",
    "plateau_delta",
    "zero_dropout_val_loss",
    "zero_minus_best",
    "best_nonzero_dropout",
    "best_nonzero_val_loss",
    "zero_minus_best_nonzero",
    "max_dropout",
    "max_dropout_val_loss",
    "max_dropout_minus_best",
    "has_nonzero_optimum",
    "meets_target_dropout",
    "curve_json",
]


def write_jsonl_row(handle, row: dict) -> None:
    handle.write(json.dumps(row, sort_keys=True) + "\n")
    handle.flush()


def metric_key(row: dict) -> tuple:
    return (
        row["run_mode"],
        row["condition"],
        row["condition_kind"],
        row.get("stage"),
        int(row["token_limit"]),
        row["model_name"],
        int(row["n_layer"]),
        int(row["n_head"]),
        int(row["n_embd"]),
        int(row["seed"]),
        float(row["dropout_initial"]),
        float(row["dropout_final"]),
        row["dropout_schedule"],
    )


def planned_metric_key(
    *,
    mode: str,
    condition: DropoutCondition,
    model_spec: ModelSpec,
    seed: int,
    token_limit: int,
    stage: int | None = None,
) -> tuple:
    return (
        mode,
        condition.name,
        condition.kind,
        stage,
        int(token_limit),
        model_spec.name,
        int(model_spec.n_layer),
        int(model_spec.n_head),
        int(model_spec.n_embd),
        int(seed),
        float(condition.initial),
        float(condition.final),
        condition.schedule,
    )


def load_metrics(path: Path) -> list[dict]:
    if not path.exists():
        return []
    rows = []
    for line in path.read_text(encoding="utf-8").splitlines():
        if line.strip():
            rows.append(json.loads(line))
    return rows


def summarize(rows: list[dict]) -> list[dict]:
    groups: dict[tuple, list[dict]] = defaultdict(list)
    for row in rows:
        key = (
            row["run_mode"],
            row["condition"],
            row["condition_kind"],
            row["stage"],
            row["token_limit"],
            row["model_name"],
            row["n_layer"],
            row["n_head"],
            row["n_embd"],
            row["parameters"],
            row["dropout_initial"],
            row["dropout_final"],
            row["dropout_schedule"],
        )
        groups[key].append(row)

    summary: list[dict] = []
    for group_rows in groups.values():
        first = group_rows[0]
        item = {field: first[field] for field in SUMMARY_FIELDS if field in first}
        item["n"] = len(group_rows)
        for source, mean_key, std_key in [
            ("train_eval_loss", "mean_train_eval_loss", "std_train_eval_loss"),
            ("val_eval_loss", "mean_val_eval_loss", "std_val_eval_loss"),
            ("generalization_gap", "mean_generalization_gap", "std_generalization_gap"),
        ]:
            values = [float(row[source]) for row in group_rows]
            item[mean_key] = statistics.fmean(values)
            item[std_key] = statistics.stdev(values) if len(values) > 1 else 0.0
        summary.append(item)
    return sorted(
        summary,
        key=lambda row: (
            row["run_mode"],
            row["model_name"],
            row["token_limit"],
            row["condition"],
            row["stage"] or -1,
        ),
    )


def write_csv(path: Path, rows: list[dict], fieldnames: list[str]) -> None:
    with path.open("w", newline="", encoding="utf-8") as handle:
        writer = csv.DictWriter(handle, fieldnames=fieldnames, extrasaction="ignore")
        writer.writeheader()
        writer.writerows(rows)


def build_model_selection(summary: list[dict], args: argparse.Namespace) -> list[dict]:
    groups: dict[tuple, list[dict]] = defaultdict(list)
    for row in summary:
        if row["condition_kind"] == "static" and row["stage"] is None:
            groups[(row["run_mode"], row["token_limit"], row["model_name"])].append(row)

    selection: list[dict] = []
    for (run_mode, token_limit, model_name), rows in groups.items():
        curve = sorted(rows, key=lambda row: row["dropout_initial"])
        best = min(curve, key=lambda row: row["mean_val_eval_loss"])
        plateau = [
            row for row in curve
            if row["mean_val_eval_loss"] <= best["mean_val_eval_loss"] + args.plateau_delta
        ]
        zero = next((row for row in curve if row["dropout_initial"] == 0.0), None)
        nonzero = [row for row in curve if row["dropout_initial"] > 0.0]
        best_nonzero = min(nonzero, key=lambda row: row["mean_val_eval_loss"]) if nonzero else None
        max_dropout = max(curve, key=lambda row: row["dropout_initial"])

        zero_loss = zero["mean_val_eval_loss"] if zero else None
        zero_minus_best = zero_loss - best["mean_val_eval_loss"] if zero_loss is not None else None
        zero_minus_best_nonzero = (
            zero_loss - best_nonzero["mean_val_eval_loss"]
            if zero_loss is not None and best_nonzero is not None
            else None
        )
        max_minus_best = max_dropout["mean_val_eval_loss"] - best["mean_val_eval_loss"]
        has_nonzero_optimum = (
            best["dropout_initial"] > 0.0
            and zero_minus_best is not None
            and zero_minus_best >= args.min_nonzero_margin
            and max_minus_best >= args.min_high_dropout_margin
        )
        curve_json = json.dumps(
            [
                {
                    "dropout": row["dropout_initial"],
                    "mean_val_loss": row["mean_val_eval_loss"],
                    "std_val_loss": row["std_val_eval_loss"],
                    "mean_train_loss": row["mean_train_eval_loss"],
                    "mean_generalization_gap": row["mean_generalization_gap"],
                    "n": row["n"],
                }
                for row in curve
            ],
            sort_keys=True,
        )
        selection.append(
            {
                "run_mode": run_mode,
                "token_limit": token_limit,
                "model_name": model_name,
                "n_layer": best["n_layer"],
                "n_head": best["n_head"],
                "n_embd": best["n_embd"],
                "parameters": best["parameters"],
                "n": best["n"],
                "best_dropout": best["dropout_initial"],
                "best_val_loss": best["mean_val_eval_loss"],
                "best_val_std": best["std_val_eval_loss"],
                "plateau_start_dropout": min(row["dropout_initial"] for row in plateau),
                "plateau_end_dropout": max(row["dropout_initial"] for row in plateau),
                "plateau_delta": args.plateau_delta,
                "zero_dropout_val_loss": zero_loss,
                "zero_minus_best": zero_minus_best,
                "best_nonzero_dropout": (
                    best_nonzero["dropout_initial"] if best_nonzero else None
                ),
                "best_nonzero_val_loss": (
                    best_nonzero["mean_val_eval_loss"] if best_nonzero else None
                ),
                "zero_minus_best_nonzero": zero_minus_best_nonzero,
                "max_dropout": max_dropout["dropout_initial"],
                "max_dropout_val_loss": max_dropout["mean_val_eval_loss"],
                "max_dropout_minus_best": max_minus_best,
                "has_nonzero_optimum": has_nonzero_optimum,
                "meets_target_dropout": best["dropout_initial"] >= args.target_min_dropout,
                "curve_json": curve_json,
            }
        )
    return sorted(
        selection,
        key=lambda row: (
            not row["has_nonzero_optimum"],
            not row["meets_target_dropout"],
            row["best_val_loss"],
        ),
    )