move inject_lora() before load_state_dict in _before_wrap so that
LoRA adapter weights from a checkpoint are properly restored on
training resume. Previously, inject happened after load, causing
lora_A/lora_B keys to be silently ignored (strict=False).
Co-Authored-By: ccx1324 <2424441089@qq.com>
When `inject_lora()` replaces Linear layers with LoRALinear after the model
has been moved to CUDA, the new lora_A and lora_B parameters were always
created on CPU, causing a device mismatch error during the forward pass.
Now lora_A and lora_B are created on the same device and dtype as the
parent weight, matching the model's current device.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
- 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)
- train_context.py referenced non-existent cfg.batch_size, replaced with cfg.batch_per_device
- default group_size lowered from 8 to 1: without a group concept (DPO), scheduler batch equals batch_per_device; rollout-based DPO can opt in via extra_kwargs['group_size']>=2
- inline expressions (rollout_batch_size, max_seq_len) extracted for readability
- add tests/trainer/test_online_e2e.py: end-to-end online_dpo via Trainer.train, exercising KV-cache-backed rollout path
- SamplingPipeline.sample gains return_logprobs; both greedy and multinomial paths now share a single log_softmax+gather instead of duplicating the sampling logic
- module-level sample() becomes a thin forwarder instead of re-implementing the three-branch logic
- eliminates ~10 lines of duplicated softmax/gather code; no caller-facing API change
- RolloutGenerator now delegates prefill/decode to InferenceScheduler.run_batch (sync API, no background thread), sharing one KV-cache code path with the inference server and eliminating O(n^2) recompute in rollout
- Add sample(return_logprobs=) and Executor.execute_decode(return_logprobs=) to expose behaviour-policy log-probs through the engine; Task gains output_logprobs
- RolloutResult now subclasses RawRollout (adds rewards only), removing duplicated fields
- RolloutRunner.__call__ returns (result, is_fresh) instead of relying on object identity, removing the fragile refresh-detection contract
- Remove O(n^2) generate_responses helper and dead code (_tokenize_prompts, unused old_model arg)
- train_context.py wires InferenceScheduler directly instead of hand-rolling SamplingPipeline
- Tests: +11 covering return_logprobs, run_batch, and KV-cache-backed rollout semantics; 404 pass
- 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__
- 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
ccx1324andccx
ViperEkura
ViperEkura
