ViperEkura 74b9308883 refactor: pass model_fn/optimizer_fn to executor.prepare ...

- 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

2026-07-20 01:32:05 +08:00

..

__init__.py

chore : 升级 torch 2.11.0+cu128，移除自定义 Muon，修复 gloo device_id

2026-06-27 16:10:37 +08:00

metric_util.py

refactor : grad_norm 指标简化，clip_grad_norm 移至 executor

2026-06-30 14:59:43 +08:00

schedule.py

fix: scheduler division-by-zero, loss_mask bool

2026-07-03 22:04:55 +08:00

strategy.py

refactor: remove redundant strategy/executor code

2026-07-19 12:45:58 +08:00

train_callback.py

fix: init last_ckpt_step and last_log_flush_step from context.optimizer_step

2026-07-15 22:15:55 +08:00

train_context.py

refactor: pass model_fn/optimizer_fn to executor.prepare

2026-07-20 01:32:05 +08:00

trainer.py

feat: add --resume flag to decouple weight loading from training resumption

2026-07-16 14:23:23 +08:00