ViperEkura 6f49738991 feat: auto-select best available attention backend ...

- Default backend resolves to highest-priority available: flash -> cuda -> torch
- attention() falls back per-call for training/fp32/unsupported head_dim
- Re-apply index_copy_ for CUDA KV cache writes (index_put_ race mitigation)

2026-08-07 13:48:59 +08:00

..

config

feat: add moe auxloss and metrics

2026-08-05 18:12:28 +08:00

dataset

refactor: remove H5 storage backend in favor of mmap bin

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

extension

feat: auto-select best available attention backend

2026-08-07 13:48:59 +08:00

inference

feat: add CUDA-graph capture for decode forward

2026-08-06 19:57:12 +08:00

model

refactor: stateless MoE routing with grouped dispatch

2026-08-05 18:42:12 +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

refactor: stateless MoE routing with grouped dispatch

2026-08-05 18:42:12 +08:00

__init__.py

chore: bump version to 1.3.12

2026-08-01 09:22:16 +08:00

factory.py

refactor: simplify BaseFactory and separate ModelFactory from AutoModel

2026-07-30 09:38:20 +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