ViperEkura c6a82a5029 refactor: align linear backward dtype with weight ...

- cast gradients and inputs to weight.dtype instead of hardcoded bf16
- single code path covers bf16 and fp32 models, no branch needed
- gradient dtype now matches the leaf parameter dtype exactly

2026-08-14 01:01:58 +08:00

..

config

feat: add persistent DataLoader workers

2026-08-09 11:38:50 +08:00

dataset

feat: add streaming IterableDataset for pretraining

2026-08-08 16:15:10 +08:00

extension

refactor: align linear backward dtype with weight

2026-08-14 01:01:58 +08:00

inference

feat: propagate attention backend across scheduler threads

2026-08-09 13:32:40 +08:00

model

refactor: split infer core into subpackages by concern

2026-08-08 23:43:05 +08:00

optim

fix: use torch.optim.AdamW in ManoAdamW instead of NAdamW

2026-08-01 09:20:54 +08:00

parallel

fix: broadcast ref/old model state_dict for FSDP

2026-07-31 08:32:22 +08:00

preprocessing

fix : correct prefill sampling and record alignment

2026-08-05 22:20:29 +08:00

serialization

refactor: remove H5 storage backend in favor of mmap bin

2026-07-29 12:50:27 +08:00

tokenize

refactor: simplify BaseFactory and separate ModelFactory from AutoModel

2026-07-30 09:38:20 +08:00

trainer

fix: report gradient snr in db

2026-08-09 13:40:27 +08:00

__init__.py

refactor: centralize logging in astrai.logging, replace ASTRAI_TIMED with log level

2026-08-08 12:39:27 +08:00

factory.py

refactor: simplify BaseFactory and separate ModelFactory from AutoModel

2026-07-30 09:38:20 +08:00

logging.py

refactor: split infer core into subpackages by concern

2026-08-08 23:43:05 +08:00

protocols.py

refactor: 重构训练后端为 Executor 模式

2026-05-24 20:35:44 +08:00

signal_handler.py

refactor: move signal_handler from parallel/ to top-level for broader reuse

2026-07-28 10:36:17 +08:00