[train] VSA-H3 DMD2 overfit: per-role backends, VSA metadata wiring, wandb guard
- MiniMaxH3Model: allow per-role attention backends (TORCH_SDPA/FLASH_ATTN/
VIDEO_SPARSE_ATTN_H3); predict_noise routes attn_metadata_vsa for
attn_kind='vsa' (dense roles keep None).
- MiniMaxH3DMDModel.prepare_batch builds real VSA-H3 metadata (packed
text|cond|audio prefix segments via _h3_vsa_prefix_segments, sparsity from
training.vsa.sparsity) when the role runs VIDEO_SPARSE_ATTN_H3.
- VSA-H3 tile(): fresh buffer for grad-tracking forwards; shared-buffer
reuse trips autograd's in-place version check at student backward.
- build_tracker: degrade to DummyTracker when wandb is unimportable or has
no credentials (modular stack already logs per-step loss dicts via the
wandb tracker; no new callback needed).
- dmd2_vsa0_overfit.yaml: student VSA-H3 @ sparsity 0.0, teacher/critic
FLASH_ATTN, 3-step DMD schedule, wandb project h3-dmd2-vsa.
- preprocess_minimax_h3_overfit: --video/--prompt/--output-dir/--model-path
CLI; overfit_vsa0.sh: preprocess + 4-GPU torchrun with FASTVIDEO_FA4=1.
- CPU tests: VSA metadata build/routing, per-role backend override,
wandb guard + loss-dict logging, tile-under-grad regression.
SolitaryThinker
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