- config: seq_len=128, batch_size=4 for long-sequence TBPTT
- dataset: create_tbptt_loader with non-overlapping windows, strict temporal order
- model: forward() accepts/exposes hidden state h; add step() for single-frame stateful inference
- train: carry detached hidden state across batches, reset at epoch boundary
- benchmark: fix model call for new (v_body, h) return signature
Generated by Mistral Vibe.
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>
- model outputs (B, S, 2) instead of (B, 2) — GRU output at every timestep
- train/val loss computed over all S timesteps with reduction=none
- benchmark/evaluate.py takes pred[:, -1, :] for final-step evaluation
- added per-step loss logging (8 evenly spaced steps) to TensorBoard
Generated by Mistral Vibe.
Co-Authored-By: Mistral Vibe <vibe@mistral.ai>
hexone2086