291 lines
12 KiB
Python
291 lines
12 KiB
Python
# utils/dataset_helpers.py
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from __future__ import annotations
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import logging, os
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from pathlib import Path
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from collections import Counter, defaultdict
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from typing import Collection, Optional
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from datetime import datetime, timezone
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import json
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import numpy as np
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from datasets import load_dataset
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logger = logging.getLogger(__name__)
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# tokens we want to watch closely
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_WATCH = [" nodded", " leaned"]
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def load_ftpo_multi_dataset(
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path: Path,
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tokenizer,
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*,
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experiment_run_dir: Path | None = None,
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max_seq_len: int = 4096,
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rejected_reg_strength: float = 0.0,
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chosen_reg_strength: float = 0.0,
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min_chosen_tokens: int = 1,
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max_train_examples: int | None = None,
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stop_words: Optional[Collection[str]] = None,
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num_proc: int | None = None,
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batch_size: int = 512,
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):
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"""
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Parallel loader for “multi-chosen” FTPO JSONL with dual regularisation.
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Logs the counts of `_WATCH` tokens at every major stage.
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"""
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if min_chosen_tokens < 1:
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min_chosen_tokens = 1
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# ------------------------------------------------------------------
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# helpers
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# ------------------------------------------------------------------
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rng = np.random.default_rng(3407)
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def _median_threshold(cts: Counter[str], strength: float) -> dict[str, float]:
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if not cts or strength <= 0:
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return {}
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med = float(np.median(list(cts.values())))
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return {t: 1.0 if c <= med else (med / c) ** strength for t, c in cts.items()}
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def _log_top(cts: Counter[str], what: str) -> None:
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head = ", ".join(f"{tok!r}:{cnt}" for tok, cnt in cts.most_common(20))
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logger.info(f"[ftpo-loader] {what} top-20 → {head}")
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logger.info(
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" ↳ watch «%s»: %s «%s»: %s",
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_WATCH[0], cts[_WATCH[0]],
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_WATCH[1], cts[_WATCH[1]],
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)
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# ------------------------------------------------------------------
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# stop-word list (unchanged)
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# ------------------------------------------------------------------
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if stop_words is None:
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stop_words = {
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"the","a","an","in","on","at","by","for","to","of","and","or","but",
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"if","then","else","when","where","how","why","what","who","whom",
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"this","that","these","those","is","are","was","were","be","being",
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"been","have","has","had","do","does","did","will","would","shall",
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"should","can","could","may","might","must"
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}
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stop_words = {w.lower() for w in stop_words}
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# ------------------------------------------------------------------
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# 0️⃣ raw load + shuffle
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# ------------------------------------------------------------------
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raw = load_dataset("json", data_files=str(path), split="train").shuffle(seed=3407)
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rows = list(raw)
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if not rows:
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raise ValueError(f"{path} contained no rows")
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rej_counts = Counter(r["rejected_decoded"] for r in rows)
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_log_top(rej_counts, "BEFORE")
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# ────────────────────────────────────────────────────────────────
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# 1️⃣ Capture ORIGINAL rejected-token distribution & ratios
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# (no rows removed, no chosen trimming yet)
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# ────────────────────────────────────────────────────────────────
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rej_cts_orig = Counter(r["rejected_decoded"] for r in rows)
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_log_top(rej_cts_orig, "PRE-NORMALISATION")
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# convert to fractional “weights” via median-threshold regularisation
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med = float(np.median(list(rej_cts_orig.values())))
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w_rej = {tok: 1.0 if c <= med else (med / c) ** rejected_reg_strength
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for tok, c in rej_cts_orig.items()}
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# normalised ratios we *want* to keep in the final dataset
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total_weighted = sum(w_rej[t] * c for t, c in rej_cts_orig.items())
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ratio_rej = {tok: (w_rej[tok] * cnt) / total_weighted
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for tok, cnt in rej_cts_orig.items()}
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# ────────────────────────────────────────────────────────────────
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# 2️⃣ Chosen-token trimming (build quotas *before* we cut)
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# ────────────────────────────────────────────────────────────────
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chosen_cts_orig = Counter(tok
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for r in rows
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for tok in (r["multi_chosen_decoded"] or []))
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_log_top(chosen_cts_orig, "ORIGINAL CHOSEN TOKENS")
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# Trim the peak: cap top tokens to match the 10th highest count
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if len(chosen_cts_orig) >= 10:
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top_counts = sorted(chosen_cts_orig.values(), reverse=True)
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cap_value = top_counts[9] # 10th highest count
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chosen_cts_capped = Counter()
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for tok, cnt in chosen_cts_orig.items():
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chosen_cts_capped[tok] = min(cnt, cap_value)
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else:
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chosen_cts_capped = chosen_cts_orig.copy()
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# Now calculate regularization on the capped distribution
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med_chosen = float(np.median(list(chosen_cts_capped.values())))
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w_chosen = {tok: 1.0 if c <= med_chosen
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else (med_chosen / c) ** chosen_reg_strength
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for tok, c in chosen_cts_capped.items()}
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tgt_chosen = {tok: int(round(c * w_chosen.get(tok, 1.0)))
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for tok, c in chosen_cts_capped.items()}
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# Log the target quotas
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quota_items = sorted(tgt_chosen.items(), key=lambda x: x[1], reverse=True)[:20]
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quota_str = ", ".join(f"{tok!r}:{quota}" for tok, quota in quota_items)
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logger.info(f"[ftpo-loader] CHOSEN TARGET QUOTAS top-20 → {quota_str}")
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logger.info(
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" ↳ watch quotas «%s»: %s (was %s) «%s»: %s (was %s)",
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_WATCH[0], tgt_chosen.get(_WATCH[0], 0), chosen_cts_orig.get(_WATCH[0], 0),
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_WATCH[1], tgt_chosen.get(_WATCH[1], 0), chosen_cts_orig.get(_WATCH[1], 0),
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)
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_log_top(Counter(r["rejected_decoded"] for r in rows), "POST-CHOSEN")
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# ────────────────────────────────────────────────────────────────
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# 3️⃣ Apply min_chosen_tokens row filter
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# ────────────────────────────────────────────────────────────────
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rows = [r for r in rows if len(r["multi_chosen_decoded"]) >= min_chosen_tokens]
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_log_top(Counter(r["rejected_decoded"] for r in rows), "POST-MIN-FILTER")
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# ────────────────────────────────────────────────────────────────
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# 4️⃣ Row-level quota sampling **now** that trimming & filtering
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# are done. Scale the original ratios to the remaining size.
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# ────────────────────────────────────────────────────────────────
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N_final = max_train_examples or len(rows)
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target_rows = {tok: int(round(ratio_rej[tok] * N_final))
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for tok in ratio_rej}
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rng.shuffle(rows)
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selected, seen = [], defaultdict(int)
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selected_indices = set() # Track indices instead of row objects
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# First pass: try to fill quotas
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for i, r in enumerate(rows):
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tok = r["rejected_decoded"]
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if seen[tok] < target_rows.get(tok, 0):
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selected.append(r)
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selected_indices.add(i)
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seen[tok] += 1
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if len(selected) >= N_final:
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break
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# Second pass: if still short, keep adding while maintaining proportions
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if len(selected) < N_final:
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# Build index of remaining rows by token
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remaining_by_token = defaultdict(list)
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for i, r in enumerate(rows):
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if i not in selected_indices:
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remaining_by_token[r["rejected_decoded"]].append((i, r))
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# Keep adding until we reach N_final
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while len(selected) < N_final:
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# Find token that's furthest below its target ratio AND has rows available
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best_tok = None
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best_ratio_diff = -1
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for tok, available_rows in remaining_by_token.items():
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if not available_rows: # Skip tokens with no remaining rows
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continue
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current_ratio = seen[tok] / len(selected) if len(selected) > 0 else 0
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target_ratio = ratio_rej.get(tok, 0)
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ratio_diff = target_ratio - current_ratio
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if ratio_diff > best_ratio_diff:
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best_ratio_diff = ratio_diff
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best_tok = tok
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# If no tokens have available rows, we're done
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if best_tok is None:
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break
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# Add one row for the most underrepresented token
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idx, r = remaining_by_token[best_tok].pop()
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selected.append(r)
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selected_indices.add(idx)
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seen[best_tok] += 1
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rows = selected
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# ── Dump the final row subset exactly as it was read (no tokenisation) ──
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if experiment_run_dir is not None:
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ts = datetime.now(timezone.utc).astimezone()\
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.strftime("%Y-%m-%d_%H-%M-%S")
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dump_file = experiment_run_dir / f"ftpo_training_set_used_{ts}.jsonl"
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try:
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with open(dump_file, "w", encoding="utf-8") as fh:
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for r in rows:
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fh.write(json.dumps(r, ensure_ascii=False) + "\n")
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logger.info("[ftpo-loader] dumped %d rows → %s", len(rows), dump_file)
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except Exception as e:
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logger.warning("[ftpo-loader] failed to dump training rows: %s", e)
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_log_top(Counter(r["rejected_decoded"] for r in rows), "AFTER-SAMPLING")
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logger.info("[ftpo-loader] kept %d rows after quota sampling", len(rows))
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# ------------------------------------------------------------------
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# 5️⃣ tokenisation (unchanged section)
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# ------------------------------------------------------------------
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from datasets import Dataset
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ds = Dataset.from_list(rows)
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tokenizer.truncation_side = "left"
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num_proc = num_proc or max(1, int(os.cpu_count() / 4))
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def _tok(batch):
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out_prompt, out_chosen, out_rej, out_valid = [], [], [], []
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prompt_tok = tokenizer(
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batch["context_with_chat_template"],
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add_special_tokens=False,
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truncation=False,
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return_attention_mask=False,
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).input_ids
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for p_ids, chosen_surf, rej_surf in zip(
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prompt_tok, batch["multi_chosen_decoded"], batch["rejected_decoded"]
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):
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chosen_surf = chosen_surf or []
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chosen_tok_ids = [tokenizer(t, add_special_tokens=False).input_ids
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for t in chosen_surf]
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rej_tok_ids = tokenizer(rej_surf, add_special_tokens=False).input_ids
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valid = (
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chosen_tok_ids
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and all(len(t) == 1 for t in chosen_tok_ids)
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and len(rej_tok_ids) == 1
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and rej_surf.strip().lower() not in stop_words
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and len(p_ids) + 1 <= max_seq_len
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)
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if valid and rej_tok_ids[0] in [t[0] for t in chosen_tok_ids]:
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valid = False
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out_valid.append(valid)
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if valid:
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out_prompt.append(p_ids)
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out_chosen.append([t[0] for t in chosen_tok_ids])
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out_rej.append(rej_tok_ids[0])
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else:
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out_prompt.append([0]); out_chosen.append([0]); out_rej.append(0)
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return {
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"prompt_ids": out_prompt,
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"chosen_ids": out_chosen,
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"rejected_token_id": out_rej,
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"__valid": out_valid,
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}
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ds = ds.map(
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_tok, batched=True, batch_size=batch_size,
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remove_columns=ds.column_names,
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num_proc=num_proc, desc="tokenising",
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)
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ds = ds.filter(lambda ex: ex["__valid"], num_proc=num_proc, desc="filter")
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ds = ds.remove_columns("__valid")
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if len(ds) == 0:
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raise ValueError("no ftpo samples survived length / sanity checks")
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return ds.shuffle(seed=3407)
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