Replace the 14 subpackages with 13 flat modules (6.7k -> 2.7k lines),
the Hydra config groups with typed dataclasses and one self-contained
YAML file per experiment (configs/), and the docs with docs/usage.md and
docs/design.md. All capabilities stay: GamGam, ttH CP, Delphes, binary
and multiclass tasks, transformer, fine-tuning from Pretrained_GNN, DDP,
torch.compile/CUDA graphs/bfloat16, predict and ONNX export.
Behavior: prepared caches, pretrained-model logits and two CPU training
epochs are bit-identical to the previous code on the fixtures and a 60k
event GamGam subset. Energies are now computed with one vectorized cosh
(<= 2.4e-7 relative difference on 969 of the full GamGam objects). The
ttH and Delphes configs now use Number % 10 as the fold (the raw event
index selected only the first 10 events), and use the paper recipe
(64/4/4, Adam 1e-4, decay 0.99). The ROOT score writer handles jagged
trees. Prediction files store source file and entry instead of sample
ids, and checkpo…
feat: upgrade to Python 3.14 and PyTorch 2.14; add torch.compile and bfloat16
- Python 3.14, torch 2.14.1 (cu130 wheels), NumPy 2; frozen parity
references pass unchanged
- trainer.compile (dynamic-shape torch.compile) and trainer.precision
(bfloat16 autocast; graph sums, loss, optimizer in float32)
- CUDA graph steps use device-tensor learning rates, so one capture per
bucket follows the schedule; checkpoints store portable optimizer state
- trainer=paper_fast: compile + CUDA graphs + bfloat16, 5.9 vs 19.2 ms per
GamGam batch for float32 eager on an A100
- loader workers fork explicitly (Python 3.14 defaults to forkserver);
ONNX export keeps the TorchScript exporter
Correction to 5b73bcb: the pre-change float32 eager step was 21.3 ms per
batch (not ~29 ms; that profile used unshuffled ttH-only batches), so the
default path improved by about 1.2x and the large gains are opt-in.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
8 days ago
feat: replace DGL with shared packed events and native PyTorch graphs
