feat: add run_pretrain.py; fix output-path naming and max_seq_len
- scripts/run_pretrain.py: single-command pre-training runner with timing estimate, loss-curve plot (matplotlib), and per-epoch report. Sets max_seq_len=256 (McGill sequences max out at 195 tokens, ~4x faster attention than the 512 default). - src/train.py: normalise --output so pretrained.pt and pretrained both produce pretrained.pt + pretrained.log.csv (not pretrained.pt.log.csv). Serialize Path fields as strings in checkpoint to satisfy weights_only. - requirements.txt: drop unused pandas/music21, add mido (pretty_midi dep). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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@@ -4,11 +4,10 @@
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# Core ML
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torch==2.12.0
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numpy==2.4.6
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pandas==3.0.3
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# Music processing
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music21==10.1.0
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pretty_midi==0.2.11
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mido==1.3.3
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# Visualization
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matplotlib==3.10.9
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"""Run full pre-training on the McGill corpus, then plot loss curves and
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print a short diagnostic report.
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Usage:
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# Full run (training + plot + report)
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python scripts/run_pretrain.py
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# Skip training if a checkpoint already exists; only re-plot and report
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python scripts/run_pretrain.py --skip-training
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Outputs written:
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checkpoints/pretrained.pt best checkpoint
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checkpoints/pretrained.log.csv per-epoch metrics
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checkpoints/pretrained_curves.png train/val loss plot
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"""
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from __future__ import annotations
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import argparse
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import csv
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import logging
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import math
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import sys
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from pathlib import Path
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import matplotlib
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matplotlib.use("Agg") # headless — no display required
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import matplotlib.pyplot as plt
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import torch
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from src.model import ChordTransformer
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from src.train import TrainConfig, train
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from src.tokenizer import TOKEN_TO_ID
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# ---------------------------------------------------------------------------
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# Paths
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# ---------------------------------------------------------------------------
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DATA_DIR = Path("data/processed")
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CHECKPOINT = Path("checkpoints/pretrained.pt")
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LOG_CSV = Path("checkpoints/pretrained.log.csv")
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CURVES_PNG = Path("checkpoints/pretrained_curves.png")
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# ---------------------------------------------------------------------------
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# Training config (mirrors the requested CLI invocation)
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# ---------------------------------------------------------------------------
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TRAIN_CFG = TrainConfig(
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data_dir=DATA_DIR,
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output=CHECKPOINT,
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epochs=50,
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batch_size=32,
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lr=3e-4,
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warmup_steps=200,
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seed=42,
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device="auto",
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# Real McGill sequences are ≤ 195 tokens (p95 = 146, mean = 92).
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# Using 256 instead of the 512 default cuts attention cost ~4x.
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max_seq_len=256,
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)
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# ---------------------------------------------------------------------------
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# Plotting
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# ---------------------------------------------------------------------------
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def plot_curves(log_csv: Path, out_png: Path) -> None:
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epochs, train_losses, val_losses, val_ppls = [], [], [], []
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with open(log_csv, newline="") as fh:
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for row in csv.DictReader(fh):
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epochs.append(int(row["epoch"]))
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train_losses.append(float(row["train_loss"]))
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val_losses.append(float(row["val_loss"]))
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val_ppls.append(float(row["val_ppl"]))
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11, 4))
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ax1.plot(epochs, train_losses, label="train loss", linewidth=1.5)
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ax1.plot(epochs, val_losses, label="val loss", linewidth=1.5)
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best_epoch = epochs[val_losses.index(min(val_losses))]
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ax1.axvline(best_epoch, color="grey", linestyle="--", linewidth=0.8,
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label=f"best epoch {best_epoch}")
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ax1.set_xlabel("epoch")
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ax1.set_ylabel("cross-entropy loss")
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ax1.set_title("Pre-training loss")
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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ax2.plot(epochs, val_ppls, color="tab:orange", linewidth=1.5)
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ax2.axvline(best_epoch, color="grey", linestyle="--", linewidth=0.8)
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ax2.set_xlabel("epoch")
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ax2.set_ylabel("perplexity")
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ax2.set_title("Val perplexity")
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ax2.grid(True, alpha=0.3)
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fig.tight_layout()
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out_png.parent.mkdir(parents=True, exist_ok=True)
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fig.savefig(out_png, dpi=150)
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plt.close(fig)
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print(f"[plot] saved → {out_png}")
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# ---------------------------------------------------------------------------
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# Report
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# ---------------------------------------------------------------------------
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def print_report(log_csv: Path, checkpoint: Path) -> None:
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rows = []
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with open(log_csv, newline="") as fh:
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rows = list(csv.DictReader(fh))
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if not rows:
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print("[report] log CSV is empty — nothing to report")
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return
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val_losses = [float(r["val_loss"]) for r in rows]
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best_idx = val_losses.index(min(val_losses))
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best_row = rows[best_idx]
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# Convergence heuristic: first epoch where val loss is within 1 % of best
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best_loss = float(best_row["val_loss"])
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conv_epoch = next(
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(int(r["epoch"]) for r in rows if float(r["val_loss"]) <= best_loss * 1.01),
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int(best_row["epoch"]),
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)
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# Parameter count from checkpoint
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n_params = None
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if checkpoint.exists():
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ckpt = torch.load(checkpoint, weights_only=True)
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mcfg = ckpt["model_config"]
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model = ChordTransformer(**mcfg)
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tied = model.token_emb.weight.numel()
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n_params = sum(p.numel() for p in model.parameters()) - tied
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print()
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print("=" * 52)
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print(" PRE-TRAINING REPORT")
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print("=" * 52)
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print(f" Total epochs run : {len(rows)}")
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print(f" Best epoch (val loss) : {best_row['epoch']}")
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print(f" Convergence epoch : {conv_epoch} (val ≤ best+1 %)")
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print(f" Best val loss : {best_loss:.4f}")
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print(f" Best val perplexity : {float(best_row['val_ppl']):.2f}")
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print(f" Final train loss : {float(rows[-1]['train_loss']):.4f}")
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if n_params is not None:
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print(f" Unique parameters : {n_params:,}")
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print(f" Checkpoint : {checkpoint}")
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print(f" Log CSV : {log_csv}")
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print("=" * 52)
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print()
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# Full epoch table for copy-paste into the report
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print(f" {'epoch':>5} {'train':>8} {'val':>8} {'ppl':>7} {'lr':>10}")
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print(f" {'-'*5} {'-'*8} {'-'*8} {'-'*7} {'-'*10}")
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for r in rows:
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marker = " ←" if int(r["epoch"]) == int(best_row["epoch"]) else ""
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print(
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f" {int(r['epoch']):>5} {float(r['train_loss']):>8.4f}"
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f" {float(r['val_loss']):>8.4f} {float(r['val_ppl']):>7.2f}"
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f" {float(r['lr']):>10.2e}{marker}"
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)
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print()
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# ---------------------------------------------------------------------------
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# Main
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# ---------------------------------------------------------------------------
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def main() -> None:
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ap = argparse.ArgumentParser(description=__doc__,
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formatter_class=argparse.RawDescriptionHelpFormatter)
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ap.add_argument("--skip-training", action="store_true",
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help="Skip training; re-plot and report from existing CSV.")
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args = ap.parse_args()
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s %(levelname)s %(message)s",
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datefmt="%H:%M:%S",
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)
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if not args.skip_training:
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if not DATA_DIR.exists():
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print(f"ERROR: data directory not found: {DATA_DIR}", file=sys.stderr)
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print("Run prepare_data.py first.", file=sys.stderr)
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sys.exit(1)
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import pathlib
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n_train = len(list((DATA_DIR / "train").glob("*.pt")))
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n_batches = (n_train + TRAIN_CFG.batch_size - 1) // TRAIN_CFG.batch_size
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# Rough estimate: ~1.5 s/batch on CPU with seq_len≈196, faster on GPU.
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est_epoch_s = n_batches * 1.5
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device_label = "GPU" if __import__("torch").cuda.is_available() else "CPU"
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print(
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f"[run_pretrain] {n_train} train files, {n_batches} batches/epoch\n"
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f"[run_pretrain] estimated time on {device_label}: "
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f"~{est_epoch_s/60:.0f} min/epoch, "
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f"~{TRAIN_CFG.epochs * est_epoch_s / 3600:.1f} h total\n"
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f"[run_pretrain] (early stopping with patience={TRAIN_CFG.patience} may reduce this)\n"
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)
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train(TRAIN_CFG)
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else:
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if not LOG_CSV.exists():
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print(f"ERROR: log CSV not found: {LOG_CSV}", file=sys.stderr)
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sys.exit(1)
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print(f"[skip-training] using existing log: {LOG_CSV}")
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plot_curves(LOG_CSV, CURVES_PNG)
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print_report(LOG_CSV, CHECKPOINT)
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if __name__ == "__main__":
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main()
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+4
-2
@@ -267,8 +267,10 @@ def train(cfg: TrainConfig) -> Path:
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# ------------------------------------------------------------------
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output_path = Path(cfg.output)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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checkpoint_path = output_path.with_suffix(".pt")
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log_csv_path = Path(str(output_path) + ".log.csv")
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# Normalise: --output may be given with or without the .pt extension.
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stem = output_path.with_suffix("") if output_path.suffix == ".pt" else output_path
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checkpoint_path = stem.with_suffix(".pt")
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log_csv_path = Path(str(stem) + ".log.csv")
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with open(log_csv_path, "w", newline="") as fh:
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writer = csv.writer(fh)
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