- 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__
- Extract shared core (mask building, primary-id extraction, tensorisation, position-id generation) to astrai/preprocessing/core.py; Pipeline and TokenizeTransform both consume it, eliminating ~60% duplicated logic
- Promote BFD _plan to module-level plan_bfd(lengths, max_len) returning pure index bins; BFDPacking.apply and evaluate_ifd._pack_bins both call it, removing the second BFD implementation
- Split Pipeline._flush (49 lines) into _inject_doc_reset_position_ids + _inject_continuous_position_ids + _to_tensors; split Pipeline.run by delegating record iteration to core.iter_raw_records
- Remove dead no-op pop/塞回 in Pipeline.run (L110-111)
- TrainConfig.max_grad_norm defaults to None
- executor.clip_grad_norm returns grad norm without clipping when None
- train.py --max_grad_norm defaults to None
- StreamStore: fetch(begin, end, key) for stream access (SEQ/SFT)
- RecordStore: mixin with fetch_record(i, key) for record access
- H5Store/MmapStore/JsonlStore now dual-inherit both (C3 MRO)
- JsonlStore supports lazy mode via processor= (no TokenizeTransform)
- RecordDataset base class holds processor, DPO/GRPO simplified
- dpo_tokenize pure function for on-the-fly JSONL tokenisation
- DatasetFactory builds processor for jsonl+record datasets
- train.py passes tokenizer_path=param_path uniformly
- progress: len(dataset) returns sample count (stream=windows, record=records)
- json no longer auto-detected as jsonl format
- Store gains fetch_record/num_records alongside stream fetch/__len__
- save_bin/load_bin support per-record offsets via record_keys param
- H5Store/MmapStore/JsonlStore all support dual stream+record access
- DPODataset/GRPODataset use fetch_record, no cross-record concat
- dpo_collate_fn + collate_fn wired through TrainConfig
- fixes attention context leakage in DPO from windowed concatenation
- stream_chat: default empty system_prompt, single-turn mode
- generate_batch: drop hardcoded system role
- generate.py: preserve original fields in messages branch
and use response_key for the output column name
- Few-shot examples now include subject preamble (consistent format)
- Add --seed flag for option permutation (default 0, -1 to disable)
- Shuffles A/B/C/D positions per-question to neutralise positional bias
- Add FrequencyPenaltyStrategy (logit -= penalty * count)
- Per-task rep_window for penalty history lookup
- Wire through engine, task, executor, API layer
- Add --frequency_penalty and --rep_window to stream_chat.py
- 9 unit tests for frequency penalty strategy
- evaluate_mmlu: fix double few-shot injection (build_prompt no longer
adds few-shot, apply_chat handles it once)
- evaluate_humaneval: fix pass@k k-filtering to be per-problem instead
of using first problem's n globally; reuse ProcessPoolExecutor across
problems; fix closure UnboundLocalError in test_one; handle None in
report when k > n
- evaluate_ifd: remove dead code (score_plain/score_messages); add
multi-file/directory input support with --input_path/--output_dir;
add summary.json aggregation and --max_samples; add --dtype flag
- evaluate_ppl: add --device and --dtype flags (was hardcoded to cuda)
- evaluate_ifeval: fix docstring path (scripts/tools -> scripts/eval)
- analyze_weights: add --output JSON export; fix dead code filter
("_norm" not in r was always True)
- Add --resume bool flag to train.py CLI
- --param_path always loads weights only by default
- --resume restores epoch, consumed_samples, optimizer & scheduler
- Checkpoint.load() now preserves full meta dict
- Update test_early_stopping to use new param_path/resume API
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