- re-register the linear family with the operator dispatcher (ASTR_OPS / op_backend / resolve)
- fix bf16 gemv misaligned-address faults and element mispairing for offset weights
- reject misaligned bf16_swiglu inputs with a clear error and fall back in the backend gate
- make the rollout reuse decision, validation, and return atomic under one policy snapshot
- add the documented post-scoring rollout version check
- derive live+1 under the scheduler lock in optimizer_step via apply_weight_update(None, ...)
- reject rollout_max_policy_lag below rollout_interval - 1 at config time
- sync gemv stream-test inputs before switching streams; drop dead loader imports
- reject prompts that encode to zero tokens in add_task instead of admitting a task whose prefill can never run, and surface empty-id run_batch calls as prompt_empty errors
- deliver the STOP stream callback when cancelling a live task so clients observe termination instead of hanging until socket timeout
- strip the torch.compile _orig_mod. prefix at every unwrap_model site and when loading checkpoints so FSDP state dicts and saved weights no longer leak the wrapper name into downstream keys
- reject online_* train strategies with nprocs > 1 at config validation time, explaining the NCCL all-gather deadlock they would otherwise hit mid-run
- apply the frequency penalty before temperature scaling (OpenAI semantics) so the penalty survives temperature=0 instead of being annihilated by the 1e8 logit blowup, and exclude penalty pipelines from the greedy fast path
- return logprobs from the raw pre-strategy distribution so they match training-side policy logprobs for PPO/GRPO importance ratios
- 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
- 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
- Replace hand-rolled BaseConfig (from_dict/to_dict/_coerce/_unwrap_optional) with pydantic.dataclasses
- from_dict now uses cls(**d), to_dict uses dataclasses.asdict + json.dumps filter
- TrainConfig: required fields are now truly required (no default=None), delete manual validate()/__post_init__
- Remove dead required() helper and metadata={'help': ...} annotations
- Fix gradient_checkpointing_modules type from List[str] to List[type]
- Add pydantic>=2.0 as direct dependency in pyproject.toml
- Add numpy-style Parameters docstrings to all config classes
- Enable use_attribute_docstrings in BaseConfig for schema generation
- LoRAConfig also migrated to pydantic dataclass
- 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
- 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
- Remove separate ValidationCallback, merge into MetricCallback
- Progress bar now tracks optimizer steps instead of micro-steps
- Remove unused log_interval config field and CLI flag
- Fix validation all_reduce: use SUM(loss, count) instead of AVG
- Simplify metric logging: always log every optimizer step
- Add grad_norm display to progress bar
ViperEkura
0z5a
QueenAmish