nucleic-purpose-classifier/cache_teacher.py

#!/usr/bin/env python3
"""Cache float-teacher logits for memory-bounded QAT distillation."""

from __future__ import annotations

import argparse
import sys
import time
from pathlib import Path
from typing import Any, Sequence

from purpose_data import LABELS, DataError, load_jsonl
from train import (
    DEFAULT_DATASET_DIR,
    DEFAULT_MODEL_REVISION,
    _select_device,
    _set_seeds,
    _validate_split,
    distillation_record_keys,
    encode_fixed_shape,
    prepare_text,
)


SCRIPT_DIR = Path(__file__).resolve().parent
DEFAULT_OUTPUT = SCRIPT_DIR / "outputs" / "purpose-lite-v1-teacher-logits.pt"


def _predict(
    torch: Any,
    model: Any,
    tokenizer: Any,
    records: Sequence[dict[str, Any]],
    *,
    device: Any,
    batch_size: int,
    progress_steps: int,
    label: str,
) -> Any:
    rows = []
    batches = (len(records) + batch_size - 1) // batch_size
    started = time.perf_counter()
    model.eval()
    with torch.inference_mode():
        for batch_index, start in enumerate(range(0, len(records), batch_size), 1):
            batch = records[start : start + batch_size]
            encoded = encode_fixed_shape(
                tokenizer,
                [prepare_text(record["prompt"]) for record in batch],
                torch,
            )
            inputs = {key: value.to(device) for key, value in encoded.items()}
            rows.append(model(**inputs).logits.cpu())
            if progress_steps and (
                batch_index % progress_steps == 0 or batch_index == batches
            ):
                print(
                    f"teacher {label} step {batch_index}/{batches} "
                    f"elapsed={time.perf_counter() - started:.1f}s",
                    flush=True,
                )
    return torch.cat(rows)


def cache_teacher(args: argparse.Namespace) -> dict[str, Any]:
    try:
        import torch
        from transformers import AutoModelForSequenceClassification, AutoTokenizer
    except ImportError as exc:
        raise DataError(
            "teacher-cache dependencies are missing; install requirements.txt"
        ) from exc

    train_path = args.dataset_dir / "train.jsonl"
    validation_path = args.dataset_dir / "validation.jsonl"
    train_records = load_jsonl(train_path)
    validation_records = load_jsonl(validation_path)
    _validate_split(train_records, train_path)
    _validate_split(validation_records, validation_path)

    output = args.output.resolve()
    local_model = Path(args.model).expanduser()
    if local_model.is_dir():
        try:
            output.relative_to(local_model.resolve())
        except ValueError:
            pass
        else:
            raise DataError("teacher cache output must not overwrite the model directory")
    if output.exists() and not args.overwrite_output:
        raise DataError(f"{output}: cache exists; pass --overwrite-output intentionally")
    if output.exists() and output.is_dir():
        raise DataError(f"{output}: cache output must be a file path")
    output.parent.mkdir(parents=True, exist_ok=True)

    _set_seeds(torch, args.seed)
    device = _select_device(torch, args.device)
    options = (
        {"local_files_only": True}
        if local_model.exists()
        else {"revision": args.model_revision}
    )
    tokenizer = AutoTokenizer.from_pretrained(args.model, use_fast=True, **options)
    model = AutoModelForSequenceClassification.from_pretrained(
        args.model,
        **options,
    ).to(device)
    teacher_labels = [
        model.config.id2label.get(index, model.config.id2label.get(str(index)))
        for index in range(len(LABELS))
    ]
    if teacher_labels != list(LABELS):
        raise DataError("teacher label order does not match purpose-lite")

    train_logits = _predict(
        torch,
        model,
        tokenizer,
        train_records,
        device=device,
        batch_size=args.batch_size,
        progress_steps=args.progress_steps,
        label="train",
    )
    validation_logits = _predict(
        torch,
        model,
        tokenizer,
        validation_records,
        device=device,
        batch_size=args.batch_size,
        progress_steps=args.progress_steps,
        label="validation",
    )
    artifact = {
        "schemaVersion": 1,
        "labels": list(LABELS),
        "teacher": str(args.model),
        "trainRecordKeys": distillation_record_keys(train_records),
        "validationRecordKeys": distillation_record_keys(validation_records),
        "trainLogits": train_logits,
        "validationLogits": validation_logits,
    }
    torch.save(artifact, output)
    return {
        "trainRecords": len(train_records),
        "validationRecords": len(validation_records),
        "output": str(output),
    }


def build_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--dataset-dir", type=Path, default=DEFAULT_DATASET_DIR)
    parser.add_argument("--model", required=True)
    parser.add_argument("--model-revision", default=DEFAULT_MODEL_REVISION)
    parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT)
    parser.add_argument("--device", default="auto")
    parser.add_argument("--seed", type=int, default=20260730)
    parser.add_argument("--batch-size", type=int, default=64)
    parser.add_argument("--progress-steps", type=int, default=50)
    parser.add_argument("--overwrite-output", action="store_true")
    return parser


def main(argv: Sequence[str] | None = None) -> int:
    parser = build_parser()
    args = parser.parse_args(argv)
    if args.batch_size <= 0:
        parser.error("--batch-size must be positive")
    if args.progress_steps < 0:
        parser.error("--progress-steps must be non-negative")
    try:
        metrics = cache_teacher(args)
    except (DataError, OSError, ValueError, RuntimeError) as exc:
        print(f"error: {exc}", file=sys.stderr)
        return 1
    print(
        f"Cached teacher logits for {metrics['trainRecords']} train and "
        f"{metrics['validationRecords']} validation records at {metrics['output']}."
    )
    return 0


if __name__ == "__main__":
    raise SystemExit(main())