GitLab

* [chat] strategy refactor unwrap model

* [chat] strategy refactor save model

* [chat] add docstr

* [chat] refactor trainer save model

* [chat] fix strategy typing

* [chat] refactor trainer save model

* [chat] update readme

* [chat] fix unit test

* [chat] refactor lora

* [chat] remove lm class

* [chat] refactor save model

* [chat] refactor train sft

* [chat] fix ci

* [chat] fix ci

* [chat] ppo trainer remove useless args

* [chat] update examples

* [chat] update benchmark

* [chat] update examples

* [chat] fix sft training with wandb

* [chat] polish docstr

* [gemini] support don't scatter after inference

* [chat] update colossalai strategy

* [chat] fix opt benchmark

* [chat] update opt benchmark

* [gemini] optimize inference

* [test] add gemini inference test

* [chat] fix unit test ci

* [chat] fix ci

* [chat] fix ci

* [chat] skip checkpoint test

Co-authored-by: Yuanchen Xu <yuanchen.xu00@gmail.com>

* run the base

* working on dist ppo

* sync

* detached trainer

* update detached trainer. no maker update function

* facing init problem

* 1 maker 1 trainer detached run. but no model update

* facing cuda problem

* fix save functions

* verified maker update

* nothing

* add ignore

* analyize loss issue

* remove some debug codes

* facing 2m1t stuck issue

* 2m1t verified

* do not use torchrun

* working on 2m2t

* working on 2m2t

* initialize strategy in ray actor env

* facing actor's init order issue

* facing ddp model update issue (need unwarp ddp)

* unwrap ddp actor

* checking 1m2t stuck problem

* nothing

* set timeout for trainer choosing. It solves the stuck problem!

* delete some debug output

* rename to sync with upstream

* rename to sync with upstream

* coati rename

* nothing

* I am going to detach the replaybuffer from trainer and make it a Ray Actor. Two benefits: 1. support TP trainer. 2. asynchronized buffer operations

* experience_maker_holder performs target-revolving _send_experience() instead of length comparison.

* move code to ray subfolder

* working on pipeline inference

* apply comments

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Co-authored-by: csric <richcsr256@gmail.com>

* Update ppo.py

Fix the bug of fetching wrong batch data

* Add peft model support in SFT and Prompts training

In stage-1 and stage-3, the peft model supports are added. So the trained artifacts will be only a small lora additions instead of the whole bunch of files.

* Delete test_prompts.txt

* Delete test_pretrained.txt

* Move the peft stuffs to a community folder.

* Move the demo sft to community

* delete dirty files

* Add instructions to install peft using source

* Remove Chinese comments

* remove the Chinese comments

The function save_model should be a part of PPOTrainer.

Co-authored-by: Yuanchen Xu <yuanchen.xu00@gmail.com>

Co-authored-by: Yuanchen Xu <yuanchen.xu00@gmail.com>

* [zero] refactor low-level zero folder structure

* [zero] fix legacy zero import path

* [zero] fix legacy zero import path

* [zero] remove useless import

* [zero] refactor gemini folder structure

* [zero] refactor gemini folder structure

* [zero] refactor legacy zero import path

* [zero] refactor gemini folder structure

* [zero] refactor gemini folder structure

* [zero] refactor gemini folder structure

* [zero] refactor legacy zero import path

* [zero] fix test import path

* [zero] fix test

* [zero] fix circular import

* [zero] update import