- Add broadcast_state_dict to sync state_dict from rank-0 to all ranks
- Fix create_ref_model returning None on non-rank-0 under FSDP
- Fix sync_old_model only updating old_model on rank-0 under FSDP
- Split skip_no_cuda/skip_no_kernel markers and hoist to top-level conftest
- Add distributed tests for broadcast_state_dict and create_ref_model
- Move create_ref_model from astrai/parallel/utils.py into executor.py
- Remove unused ColumnParallelLinear/RowParallelLinear (module.py)
- Update imports in strategy.py and train_context.py
- Drop unused astrai.parallel.utils and astrai.parallel.module
- unshard() and full_tensor() are collective ops, all ranks must participate
- Old code returned None on non-rank-0 before calling unshard, causing deadlock
- Fix: all ranks unshard/full_tensor, only rank-0 keeps the result
- Move create_ref_model to parallel/utils.py, accept executor+model directly
- Guard create_ref_model and sync_old_model against None on non-rank-0
- New FSDP2Executor registers as 'fsdp2' in ExecutorFactory, using per-module fully_shard() instead of FSDP1 FlatParameter wrapper
- FSDP2 preserves original Parameter objects as DTensors, eliminating use_orig_params=True hack
- FSDP2Executor implements _no_sync via set_requires_gradient_sync, clip_grad_norm via unshard, unwrap_model via DTensor.full_tensor
- Drop **_extra/**_ddp_only_kwargs fallbacks in BaseExecutor/FSDPExecutor/FSDP2Executor, replaced by parallel_mode-aware executor_kwargs dispatch in train.py (ddp-only kwargs only passed when parallel_mode=ddp)
- Export FSDP2Executor in astrai.parallel.__init__
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