- BaseExecutor.prepare now takes factories and instantiates model via model_fn(), runs before_wrap hook, wraps DDP/FSDP, then builds optimizer/scheduler on the wrapped model
- optimizer/scheduler creation moved into executor.prepare, eliminating the old 'create-then-wrap' hack reliance on use_orig_params=True
- FSDPExecutor/BaseExecutor accept **_extra kwargs to tolerate DDP-only keys (broadcast_buffers, gradient_as_bucket_view) being forwarded via executor_kwargs
- dataloader builds stay external; executor only handles model/optimizer/scheduler
- train_context.py rewritten to load checkpoint state_dict before prepare via a before_wrap closure
- Store owns window_size/stride and __getitem__/__len__/sample_window
- Dataset classes become thin delegators binding a Store to a train-type key mapping
- Drop BaseDataset.get_index and the RecordDataset中间类 (window死代码)
- DatasetFactory forces window_size=0 for record datasets so record semantics never get window-tainted
- token_count/num_records split the legacy len() semantics (raw stream length vs record count)
- Update tests to the new .store/.token_count API and window/record mode switching
- Drop BaseStrategy.model_fn (stored but never read)
- Drop model_fn= passed to StrategyFactory.create in train_context
- Simplify FSDPExecutor.clip_grad_norm None branch to delegate to super()
- Remove DDPExecutor._gather_state_dict override (identical to base)
- 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)
- ChatTemplate: defer Jinja2 compilation to cached_property, exclude compiled template from __getstate__ (its dynamic root function has __module__=None and falls back to __main__, breaking pickle)
- AutoTokenizer: bypass __getattr__ for underscore-prefixed attrs to prevent infinite recursion during unpickle when __dict__ is empty
- 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
- dpo_tokenize now uses tokenizer.apply_chat_template to match SFT format
- Prompt rendered with add_generation_prompt=True
- Chosen/rejected appended as assistant turn
- Remove leftover dead code from _extract_text
- Update tests to mock apply_chat_template
- DPO requires sequence-level sum of token logprobs, not per-token mean
- mean reduction made beta*ratio_diff ~0.03 (near-zero gradient)
- loss stalled at 0.6931 because logsigmoid(0.03) has vanishing grad
- sum gives beta*ratio_diff ~10 with meaningful gradients
- load(store=...) binds directly, skipping format detection/processor
- load_path now optional when store is given
- Remove redundant from_store (merged into load)
- Caller can fully control Store construction + processor setup
- StreamStore/RecordStore → Streamable/Recordable (stateless mixins)
- Store is sole base class, no MRO ambiguity
- H5Store/MmapStore/JsonlStore mix in both traits explicitly
- segments_are_records declared per-subclass (H5/Jsonl=True, bin=False)
- Add tests for dpo_tokenize, lazy jsonl, dual-mode H5, stream-only bin
- Remove unused _to_tensor helper
- 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
- Extract tokenization/mask/position logic from JsonlStore into TokenizeTransform
- JsonlStore now pure reader: reads JSON records, delegates to transform
- Store no longer imports tokenizer or preprocessing components
- Replace per_record param with segments_are_records class attribute
- Store subclasses declare segment semantics as format-level property
- 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)
- BFDSplitPacking splits over-length sequences into chunks before BFD
- All keys (loss_mask, position_ids, ...) split in lockstep for alignment
- No tokens lost vs bfd which truncates over-length sequences
- Tests: token preservation, chunk alignment, short unchanged, vs bfd
- 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
- Replace is_causal + causal_offset with unified causal_offset (-1 = off, >=0 = first Q pos)
- Causal and mask can now coexist (was mutually exclusive)
- Add stride-based addressing for Q/KV/O (layout-agnostic, zero-copy)
- Add layout param ("bhld"/"blhd") parsed in Python, passed as int to C++
- Support 2D [batch, kv_len] and 3D [batch, q_len, kv_len] mask
- Vectorize paged KV gather in Python fallback (was per-token Python loop)
- Extract shared helpers: compute_num_splits, alloc_split_partials, dispatch_head_dim
- Unify paged_decode entry via attn_pack_paged_params
- Update mma_softmax_tile for 3D mask with per-row qrow indexing
- Mask prompt tokens to 0 so their logprobs excluded from ratio/KL
- Switch to token-level ratio + PPO clipping via reduction='none'
- Slice response token logprobs from full sequence output
- Replace k3 KL estimator with non-negative k1 estimator
- Fix epsilon from finifo.eps (~1e-38) to 1e-8
- Remove unused 'reduction' param from GRPOStrategy.__init__
- Clarify offline batch semantics in docstring
- Add 11 unit tests for masking, advantage, KL, sync, clipping
- Sync training.md and architecture.md docs
- Remove MIN_BLOCKS template param and __launch_bounds__ attribute
- Profiling shows smem (not registers) is the occupancy bottleneck for D>=64, making the hint a no-op
- D=64 sees 2-4% speedup, D=128 unchanged (smem-capped at 1 block/SM)
- Update comment blocks in kernel header and both dispatch sites
- Verified correctness via standalone CUDA test (max_err ~1e-4)
- Double-buffered KV (STAGES=2) for D<=128: next tile cp.async overlaps current tile MMA compute, hiding global load latency
- Q loaded directly from global into mma A-operand registers, removing sQ staging and prologue syncwarp
- Predicated cp.async unifies full and partial tile paths, eliminating scalar fallback branch
- STAGES=1 fallback for D=256 (double-buffer would exceed smem budget)
- Applied to both contiguous and paged decode MMA kernels
- ~1.27x average speedup on L20 (sm_89), zero precision loss
- Add attn_paged_decode wrapper in ops.py with gather fallback
- Register kernel in loader.py and export from __init__.py
- Extract test_utils.cuh shared by all attention unit tests
- Rename attn_paged_vs_contiguous.cu to attn_paged_decode_test.cu
- Refactor decode/prefill tests to use common bf16 helpers and cpu ref
- Fix k_cache dim check in attn_paged_decode.cu
- PagedAttentionParams merged into attn_common.h
- Scalar variant: warp-per-query-head split-KV, resolves page table per-position for the K/V shared-memory tile load
- MMA variant (sm_80+): tensor-core head-packing with cp.async, single page-table lookup per tile (BC=32 fits within page_size>=32)
- Standalone test: 14 cases across head_dim 32/64/128/256, GQA, multi-batch, both paths verified against CPU reference
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