- Add astrai.serialization.hf_adapter mapping LLaMA-style HF keys to AstrAI names (input_layernorm, gate_proj, MoE experts/shared_experts) with config aliases for dense and MoE (Mixtral/DeepSeek-V3) layouts; reject biased projections, mismatched head_dim and MLA
- Give AutoModel.from_pretrained weights_format=auto|astrai|hf with auto-detection; read sharded safetensors via model.safetensors.index.json
- Adapt preloaded weights/config in train_context and benchmark CLI
- avoid constructing model_fn more than once when reading config
- keep inference package exports focused on public entry points
- rename extra strategy arguments to strategy_kwargs
- 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
- keep training attention on dense 4d tensors
- use packed 3d tensors with KV cache for inference
- extend CUDA rotary embedding to packed 3d inputs
- adapt torch, CUDA and FlashAttention backend dispatch
- 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
- rewrite docker.sh with gpu default and --no-gpu override
- inject host uid/gid via ASTRAI_UID/GID in train.sh compose()
- filter readonly UID/GID lines when sourcing .env.train
- build image user via USER_UID/USER_GID args matching host uid/gid
- pass all GPUs (count: all) and filter by CUDA_VISIBLE_DEVICES inside the container
- forward NCCL vars through compose environment
- add a GPU trainer Compose profile with mounted data, models, and checkpoints
- add host commands for preflight, lifecycle, logs, status, and checkpoint cleanup
- resume from the latest complete checkpoint with external config or CLI arguments
- route decode benchmark through InferenceEngine generate path
- add enable_cuda_graph toggle to engine, scheduler, and executor
- make benchmark --cuda-graph/--no-cuda-graph control the toggle
- hoist local time imports to module top
- Eliminate core/ directory into cache/, runtime/, network/ subpackages plus flat modules
- Split cache.py (647 lines) into cache/{buffer,strategy,pool}.py by layer
- Add explicit ContiguousStrategy, make AllocationStrategy a real ABC
- Move TaskCacheState to cache/strategy.py, drop string forward references
- Rename api/ to network/, server.py to app.py
- Move sample.py into runtime/ alongside executor and graph
- Simplify TaskCacheManager.__init__ to single pool param
- Expose pool.strategy and pool.req_pool as public properties
- Fix KVCache import in attention_backend.py (TYPE_CHECKING guard)
- Fix steady-state decode reading uninitialized position_ids on first step
- TaskCacheRegistry -> TaskCacheManager (independent, held by scheduler)
- TaskCacheState co-locates 5 parallel dicts into one dataclass
- AllocationStrategy base class + PagedStrategy subclass (page_size is a parameter)
- _rollback() helper for unified cleanup (no duplicate free paths)
- Task._kv_len + prefill_done property (explicit, no output_tokens proxy)
- Steady-state detection single-sourced in TaskCacheManager.bind()
- PagePool is now pure physical layer (no task knowledge)
- Removed dead _page_to_hash dict in RadixCache
- move setup_logging to astrai/logging.py
- timed() now uses logger.isEnabledFor(DEBUG) instead of separate env var
- enable ASTR_LOG_LEVEL=DEBUG to see per-step timing logs
- call setup_logging() in stream_chat.py
- Pre-allocate decode_out in InferenceWorkspace so attn_paged_decode does not call torch::empty inside graph capture
- Wire decode_out through KVCache, PagePool.bind_tasks, and CudaBackend.fwd_decode
- Run live forward before graph capture to get valid output (graph pool memory is zeroed after capture block exits)
- Greedy generation with graph replay is bit-exact across all batch sizes
- Decode speedups vs no-graph: B=1 2.09x, B=4 1.80x, B=8 1.94x, B=16 1.76x
- Replace per-.cu-file static cached tensors with workspace-managed pre-allocated buffers
- InferenceWorkspace now owns decode_o_part / decode_ml_part (mirrors FlashInfer's workspace pattern)
- KVCache carries the buffers through the backend -> C++ kernel chain
- C++ kernels accept optional pre-allocated buffers; fallback to alloc_split_partials for backward compat
- Pre-allocates once at Executor init, zero allocation in the decode hot loop
- Prerequisite for CUDA-graph capture (all kernel addresses are stable)
- adapt bind_tasks to workspace API and reuse a stable workspace
- drop required checkpoint, randomize default 1B GQA preset
- add config override flag for arbitrary model architectures
- Drop hardcoded CACHE_MAX_SEQ=2048 which overflowed at long prompts
- Size prefill pool to prompt_length and decode pool to prompt+5+gen*num_trials
- Unblocks decode/prefill benchmark at prompt 4096+ (was KV cache index OOB)
- add GradSNRTracker to metric_util.py computing SNR = E[g]^2 / Var(g) via per-parameter EMA moments
- add grad_snr_tracker field to TrainContext (instantiated by default)
- register grad_snr in MetricCallback, update tracker on each optimizer step before metrics are recorded
- add grad_snr to default --metrics in train.py CLI
- 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 GroupedOption/GroupedCommand (no third-party dep) that tags each option with a group label and renders help in labeled sections
- add opt() shorthand wrapping click.option with cls=GroupedOption
- tag all ~55 options into 10 groups aligned with params.md chapters
- Remove H5Store, H5Writer, save_h5/load_h5 and h5py dependency
- MmapStore (bin) is the sole pre-tokenized storage backend
- Move setup_logging after imports to fix E402 in __init__.py
- Clean up unused imports across test files
- Move inline test imports to file top
- Remove --max_tokens, let scheduler use max_seq_len - prompt_len
- Rename --cache_len to --max_seq_len to match engine naming
- Unify sampling defaults to 0.8/50/0.95
- Add EOS token at end of response in both conditional and unconditional passes so model also predicts when response should end
- New --append_eos/--no-append_eos CLI flag (default: enabled) with graceful fallback when tokenizer has no EOS
- setup_logging(): attach handler only to astrai logger, not root
- all astrai.* sub-module loggers inherit automatically
- controlled by ASTR_LOG_LEVEL env var (default INFO)
- called in if __name__ == '__main__' of each CLI script
- Replace argparse with click in all scripts (train, server, generate,
preprocess, benchmark)
- Add --config YAML support to train.py with CLI flag override
- Add --dry-run mode to validate config before training
- Add type annotations throughout benchmark.py
- Unify docstring format across all commands
- Remove redundant deps httpx, requests, pyyaml, rich from pyproject.toml
- Net -346 lines while adding YAML config support
- Fix pre-existing ruff lint warnings (F401, F541, F841, E741)
- Exclude .md/.json/.yml from ruff format check
- Unblock SIGTERM/SIGINT via pthread_sigmask in early signal handler
- Do not restore SIG_DFL on unregister to prevent pending signal kills
- RolloutGenerator._instruction_to_messages builds system/user/assistant list (instruction->system, input->user, output->assistant), replacing single-user-turn concatenation
- Remove _iter_samples helper; _prepare_prompts zips parallel list-of-strings fields directly per the collate_fn contract
- Tests adopt a system-aware chat template and pin the three-field role mapping
- Drop unused imports caught by ruff F401 (torch.Tensor in scheduler.py, iter_raw_records in pipeline.py, Tuple in evaluate_rouge.py)
- 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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