- scripts/train.sh load_infra() parses the top-level infra: section of TRAIN_CONFIG_FILE
- exports TRAIN_JOB_NAME/DATA/MODEL/CHECKPOINT_DIR/TRAIN_GPU_COUNT/CUDA_VISIBLE_DEVICES
- infra overrides .env.train via compose interpolation precedence; keys absent fall back
- train.yaml is now the single per-job config: host mounts, GPU filter, and hyperparameters
- requires host python3 with PyYAML when TRAIN_CONFIG_FILE is set; errors fail fast
- docs: docker-training.md documents the infra overrides and precedence
- register yaml 1.2 float resolver so scientific notation (2e-5) becomes float, not str
- replaces the decimal-point workaround in train configs
- add containerized training doc under docs/developer
- merge AttentionParams and PagedAttentionParams into one struct
- add attn_kv_source.cuh with ContigKV/PagedKV addressing policies
- template prefill/decode kernels (MMA + scalar) on the KV policy, deleting the four duplicated attn_paged_*.cuh variants
- template dispatcher launchers on KV; single combine kernel
- verify: all correctness tests pass and SASS matches baseline (no perf regression)
- Fix rotary docs to describe cos/sin freqs_cis table, not complex buffer
- Replace attn_prefill with attn_paged_prefill for the CudaBackend path
- Register attn_paged_prefill in kernel overview, layout, and module list
- Add qo_indptr and InferenceWorkspace to architecture class diagram
- Add FrequencyPenaltyStrategy to sampling design patterns
- Thread a cudaStream_t through attn dispatchers onto torch's current stream
- Scope the device guard to the entry function so kernels run on tensor device
- DISPATCH_HEAD_DIM now forwards varargs so stream reaches each dispatch
- Parallelize CPU reference kernels with OpenMP (paged test 31s -> 7s)
- Merge decode/prefill standalone tests into attn_test.cu with correctness tables
- Drop bench error column (CPU ref too slow at large sizes)
- Update cuda_kernels.md for the merged test layout
- Converts detached strategy metrics before returning loss output
- Removes redundant item conversion from the trainer loop
- Updates the documented contract and regression tests
- Propagates MoE load-balancing loss through model outputs
- Logs task, auxiliary, and weighted losses across strategies
- Computes only explicitly requested callback metrics
- Preserves tensor compute_loss API and adds regression tests
- implement Mano (v2) with axis-rotating tangent projection and manifold normalization, replacing Newton-Schulz iteration
- composite ManoAdamW reuses partition_optimizer_parameters and composite helpers
- register mano_adamw in OptimizerFactory, export Mano and ManoAdamW
- add --mano_momentum and --mano_nesterov CLI options in Optimizer group
- add mano_adamw hyperparameters branch in train.py
- document mano_adamw in params.md
- add tests for single-step projection, axis alternation, factory registration, closure, and resume
- add astrai/optim/composite.py with shared step/zero_grad/state_dict/param_groups helpers and OptimizerFactory
- rename MuonMix to MuonAdamW (matches registered name muon_adamw) and file to muon_adamw.py
- use @OptimizerFactory.register decorator in each optimizer module instead of post-import registration in __init__
- fix closure being invoked once per sub-optimizer in MuonAdamW.step (now exactly once via composite_step)
- NoraNAdamW.step now forwards closure correctly
- Replace all buffer[layer_id][loc] double indexing with buffer[layer_id, loc] single advanced indexing in cache.py and attention.py
- Revert KVStorage buffers back to 4D [n_layers, size, n_kv_heads, head_dim], remove leftover 3D reshape/view in MLA path
- Update docs/guides/inference.md, docs/developer/internals.md, docs/developer/architecture.md to reflect new PagePool/KVStorage/ReqToTokenPool/KVCache classes
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