lars
c3b7b2744c
The dataset yielded one row at a time, forcing DataLoader's default collate to Python-loop over every row to assemble each batch. That loop scales with batch size and was pinning a CPU core at 100% while the GPU sat idle. Now the dataset yields whole batches via vectorized numpy slicing, used with DataLoader(batch_size=None). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
213 lines
7.2 KiB
Python
213 lines
7.2 KiB
Python
import argparse
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import subprocess
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import tomllib
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from pathlib import Path
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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from giant.data.loader import (
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find_parquet_files,
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load_event_ids,
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iter_file_chunks,
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build_index_maps_from_files,
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)
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from giant.data.transforms import build_features, _WelfordAccumulator
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from giant.data.dataset import make_event_split, StreamingStepsDataset
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from giant.model.network import DenoisingMLP
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from giant.train import train as run_training
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def _auto_device() -> torch.device:
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if torch.cuda.is_available():
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return torch.device("cuda")
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if torch.backends.mps.is_available():
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return torch.device("mps")
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return torch.device("cpu")
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def _git_hash() -> str:
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try:
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return subprocess.check_output(
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["git", "rev-parse", "HEAD"], stderr=subprocess.DEVNULL
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).decode().strip()
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except Exception:
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return "unknown"
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def _load_config(path: str) -> dict:
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with open(path, "rb") as f:
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return tomllib.load(f)
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def _save_config(cfg: dict, out_dir: Path) -> None:
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lines = [f"# git: {_git_hash()}", ""]
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for section, values in cfg.items():
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lines.append(f"[{section}]")
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for k, v in values.items():
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lines.append(f"{k:<12} = {repr(v) if isinstance(v, str) else v}")
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lines.append("")
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(out_dir / "config.toml").write_text("\n".join(lines))
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def main() -> None:
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parser = argparse.ArgumentParser(description="Train GIANT surrogate model")
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parser.add_argument("--config", default=None, help="Path to TOML config file")
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parser.add_argument("--data", required=True, help="Path to parquet file or directory")
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parser.add_argument("--mode", choices=["flow", "ddpm"])
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parser.add_argument("--epochs", type=int)
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parser.add_argument("--batch-size", type=int)
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parser.add_argument("--lr", type=float)
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parser.add_argument("--hidden-dim", type=int)
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parser.add_argument("--n-blocks", type=int)
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parser.add_argument("--emb-dim", type=int)
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parser.add_argument("--val-fraction", type=float)
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parser.add_argument("--shuffle-buffer", type=int, default=65536,
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help="Rows held in RAM for shuffling per worker (default: 65536)")
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parser.add_argument("--out", default=None, help="Checkpoint output directory (default: auto from hyperparams)")
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parser.add_argument("--device", default=None, help="cpu | cuda | mps (default: auto)")
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parser.add_argument("--num-workers", type=int)
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args = parser.parse_args()
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# Defaults → config file → explicit CLI flags.
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cfg: dict = {
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"train": {
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"mode": "flow", "epochs": 100, "batch_size": 4096, "lr": 3e-4,
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"val_fraction": 0.1, "num_workers": 4,
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},
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"model": {
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"hidden_dim": 256, "n_blocks": 6, "emb_dim": 16,
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},
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}
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if args.config:
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file_cfg = _load_config(args.config)
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for section in ("train", "model"):
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cfg[section].update(file_cfg.get(section, {}))
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cli_train = {k: v for k, v in {
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"mode": args.mode, "epochs": args.epochs, "batch_size": args.batch_size,
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"lr": args.lr, "val_fraction": args.val_fraction, "num_workers": args.num_workers,
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}.items() if v is not None}
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cli_model = {k: v for k, v in {
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"hidden_dim": args.hidden_dim, "n_blocks": args.n_blocks, "emb_dim": args.emb_dim,
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}.items() if v is not None}
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cfg["train"].update(cli_train)
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cfg["model"].update(cli_model)
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t, m = cfg["train"], cfg["model"]
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device = torch.device(args.device) if args.device else _auto_device()
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out_dir = Path(args.out or (
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f"checkpoints/{t['mode']}"
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f"_h{m['hidden_dim']}"
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f"_b{m['n_blocks']}"
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f"_e{m['emb_dim']}"
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f"_lr{t['lr']}"
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f"_bs{t['batch_size']}"
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))
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print(f"device: {device}")
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print(f"out_dir: {out_dir}")
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# --- Discover files ---
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files = find_parquet_files(args.data)
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print(f"found {len(files)} parquet file(s)")
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# --- Scan event IDs (single column, cheap) ---
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print("scanning event IDs …")
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all_event_ids = np.concatenate([load_event_ids(f) for f in files])
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train_events, val_events = make_event_split(all_event_ids, val_fraction=t["val_fraction"])
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n_train_steps = (np.isin(all_event_ids, np.array(sorted(train_events)))).sum()
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print(f" {len(all_event_ids):,} steps | "
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f"{len(train_events)} train events (~{n_train_steps:,} steps) | "
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f"{len(val_events)} val events")
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# --- Scan PDG / material vocabularies (2 columns, cheap) ---
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print("building vocabulary maps …")
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pdg_map, mat_map = build_index_maps_from_files(files)
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print(f" {len(pdg_map)} PDG codes | {len(mat_map)} materials")
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# --- Streaming normalizer fit over training data ---
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print("fitting normalizer (streaming) …")
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events_arr = np.array(sorted(train_events))
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cond_acc = _WelfordAccumulator(9)
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tgt_acc = _WelfordAccumulator(6)
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for path in files:
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for chunk in iter_file_chunks(path):
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mask = np.isin(chunk["event_id"], events_arr)
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if not mask.any():
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continue
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chunk_tr = {k: v[mask] for k, v in chunk.items()}
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cond_cont, _, target, _, _ = build_features(chunk_tr, pdg_map, mat_map)
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cond_acc.update(cond_cont)
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tgt_acc.update(target)
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cond_norm = cond_acc.to_normalizer()
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tgt_norm = tgt_acc.to_normalizer()
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# --- Streaming datasets ---
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train_ds = StreamingStepsDataset(
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files=files,
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split_events=train_events,
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pdg_map=pdg_map,
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mat_map=mat_map,
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cond_normalizer=cond_norm,
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target_normalizer=tgt_norm,
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batch_size=t["batch_size"],
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shuffle_buffer=args.shuffle_buffer,
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shuffle=True,
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)
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val_ds = StreamingStepsDataset(
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files=files,
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split_events=val_events,
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pdg_map=pdg_map,
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mat_map=mat_map,
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cond_normalizer=cond_norm,
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target_normalizer=tgt_norm,
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batch_size=t["batch_size"],
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shuffle=False,
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)
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# Dataset yields whole batches already, so batch_size=None tells DataLoader
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# to pass them through instead of re-collating row-by-row in Python.
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pin = device.type == "cuda"
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train_loader = DataLoader(
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train_ds, batch_size=None,
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num_workers=t["num_workers"], pin_memory=pin,
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)
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val_loader = DataLoader(
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val_ds, batch_size=None,
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num_workers=t["num_workers"], pin_memory=pin,
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)
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model = DenoisingMLP(
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pdg_vocab=len(pdg_map),
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mat_vocab=len(mat_map),
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hidden_dim=m["hidden_dim"],
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n_blocks=m["n_blocks"],
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emb_dim=m["emb_dim"],
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)
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print(f"model: {sum(p.numel() for p in model.parameters()):,} parameters")
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out_dir.mkdir(parents=True, exist_ok=True)
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_save_config(cfg, out_dir)
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run_training(
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model=model,
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train_loader=train_loader,
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val_loader=val_loader,
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mode=t["mode"],
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epochs=t["epochs"],
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lr=t["lr"],
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device=device,
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out_dir=out_dir,
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normalizer_dict={"cond": cond_norm.to_dict(), "target": tgt_norm.to_dict()},
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pdg_map={str(k): v for k, v in pdg_map.items()},
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mat_map={str(k): v for k, v in mat_map.items()},
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)
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if __name__ == "__main__":
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main()
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