GitLab

* Detached ppo (#9)

* 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

* working on pipeline strategy. in progress.

* remove pipeline code. clean this branch

* update remote parameters by state_dict. no test

* nothing

* state_dict sharding transfer

* merge debug branch

* gemini _unwrap_model fix

* simplify code

* simplify code & fix LoRALinear AttributeError

* critic unwrapped state_dict

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

* [chat] add perfomance evaluator and fix bugs (#10)

* [chat] add performance evaluator for ray

* [chat] refactor debug arg

* [chat] support hf config

* [chat] fix generation

* [chat] add 1mmt dummy example

* [chat] fix gemini ckpt

* split experience to send (#11)
Co-authored-by: csric <richcsr256@gmail.com>

* [chat] refactor trainer and maker (#12)

* [chat] refactor experience maker holder

* [chat] refactor model init

* [chat] refactor trainer args

* [chat] refactor model init

* [chat] refactor trainer

* [chat] refactor experience sending logic and training loop args (#13)

* [chat] refactor experience send logic

* [chat] refactor trainer

* [chat] refactor trainer

* [chat] refactor experience maker

* [chat] refactor pbar

* [chat] refactor example folder (#14)

* [chat] support quant (#15)

* [chat] add quant

* [chat] add quant example

* prompt example (#16)

* prompt example

* prompt load csv data

* remove legacy try

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

* [chat] add mmmt dummy example and refactor experience sending (#17)

* [chat] add mmmt dummy example

* [chat] refactor naive strategy

* [chat] fix struck problem

* [chat] fix naive strategy

* [chat] optimize experience maker sending logic

* [chat] refactor sending assignment

* [chat] refactor performance evaluator (#18)

* Prompt Example & requires_grad state_dict & sharding state_dict (#19)

* prompt example

* prompt load csv data

* remove legacy try

* maker models require_grad set to False

* working on zero redundancy update

* mmmt_prompt example; naive strategy requires_grad state_dict & sharding; maker model requires_no_grad.

* remove legacy examples

* remove legacy examples

* remove replay buffer tp state. bad design

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

* state_dict sending adapts to new unwrap function (#20)

* prompt example

* prompt load csv data

* remove legacy try

* maker models require_grad set to False

* working on zero redundancy update

* mmmt_prompt example; naive strategy requires_grad state_dict & sharding; maker model requires_no_grad.

* remove legacy examples

* remove legacy examples

* remove replay buffer tp state. bad design

* opt benchmark

* better script

* nothing

* [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

* working on lora reconstruction

* state_dict sending adapts to new unwrap function

* remove comments

---------
Co-authored-by: csric <richcsr256@gmail.com>
Co-authored-by: ver217 <lhx0217@gmail.com>

* [chat-ray] add readme (#21)

* add readme

* transparent graph

* add note background

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

* [chat] get images from url (#22)

* Refactor/chat ray (#23)

* [chat] lora add todo

* [chat] remove unused pipeline strategy

* [chat] refactor example structure

* [chat] setup ci for ray

* [chat-ray] Support LoRA trainer. LoRA weights reconstruction. (#24)

* lora support prototype

* lora support

* 1mmt lora & remove useless code

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

* [chat] fix test ci for ray

* [chat] fix test ci requirements for ray

* [chat] fix ray runtime env

* [chat] fix ray runtime env

* [chat] fix example ci docker args

* [chat] add debug info in trainer

* [chat] add nccl debug info

* [chat] skip ray test

* [doc] fix typo

---------
Co-authored-by: csric <59389055+CsRic@users.noreply.github.com>
Co-authored-by: csric <richcsr256@gmail.com>

* fix typo colossalai/autochunk auto_parallel amp

* fix typo colossalai/auto_parallel nn utils etc.

* fix typo colossalai/auto_parallel autochunk fx/passes  etc.

* fix typo docs/

* change placememt_policy to placement_policy in docs/ and examples/

* fix typo colossalai/ applications/

* fix typo colossalai/autochunk auto_parallel amp

* fix typo colossalai/auto_parallel nn utils etc.

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

* fix spelling error with examples/comminity/

* fix spelling error with tests/

* fix some spelling error with tests/ colossalai/ etc.

* [chat] add opt attn kernel

* [chat] disable xformer during fwd

* [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>

update

Update test_ci.sh

Update test_ci.sh

Update test_ci.sh

Update test_ci.sh

Update test_ci.sh

Update test_ci.sh

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update test_ci.sh

Update test_ci.sh

update

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

update ci

Update test_ci.sh

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

Update test_ci.sh

Update test_ci.sh

Update run_chatgpt_examples.yml

Update test_ci.sh

Update test_ci.sh

Update test_ci.sh

update test ci

RoBERTa for RLHF Stage 2 & 3 (still in testing)

Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"

This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368.

Add RoBERTa for RLHF stage 2 & 3

1. add roberta folder under model folder
2. add  roberta option in train_reward_model.py
3. add some test in testci

Update test_ci.sh

Revert "Update test_ci.sh"

This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a.

Add RoBERTa for RLHF Stage 2 & 3 (test)

RoBERTa for RLHF Stage 2 & 3 (still in testing)

Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"

This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368.

Add RoBERTa for RLHF stage 2 & 3

1. add roberta folder under model folder
2. add  roberta option in train_reward_model.py
3. add some test in testci

Update test_ci.sh

Revert "Update test_ci.sh"

This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a.

update roberta with coati

chat ci update

Revert "chat ci update"

This reverts commit 17ae7ae01fa752bd3289fc39069868fde99cf846.

[test]chat_update_ci

Update test_ci.sh

Update test_ci.sh

test

Update gpt_critic.py

Update gpt_critic.py

Update run_chatgpt_unit_tests.yml

update test ci

update

update

update

update

Update test_ci.sh

update

Update test_ci.sh

Update test_ci.sh

Update run_chatgpt_examples.yml

Update run_chatgpt_examples.yml

* 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

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

* mv LlamaForCausalLM to LlamaModel

* rm unused imports

---------
Co-authored-by: gongenlei <gongenlei@baidu.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.

* Add RoBERTa for RLHF Stage 2 & 3 (test)

RoBERTa for RLHF Stage 2 & 3 (still in testing)

* Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"

This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368.

* Add RoBERTa for RLHF stage 2 & 3

1. add roberta folder under model folder
2. add  roberta option in train_reward_model.py
3. add some test in testci

* Update test_ci.sh

* Revert "Update test_ci.sh"

This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a.

* Add RoBERTa for RLHF Stage 2 & 3 (test)

RoBERTa for RLHF Stage 2 & 3 (still in testing)

* Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"

This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368.

* Add RoBERTa for RLHF stage 2 & 3

1. add roberta folder under model folder
2. add  roberta option in train_reward_model.py
3. add some test in testci

* Update test_ci.sh

* Revert "Update test_ci.sh"

This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a.

* update roberta with coati

* chat ci update

* Revert "chat ci update"

This reverts commit 17ae7ae01fa752bd3289fc39069868fde99cf846.

* [Chat] fix the tokenizer "int too big to convert" error in SFT training

fix the tokenizer error during SFT training using Bloom and OPT

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

fix sft training for bloom, gpt and opt 

* Add RoBERTa for RLHF Stage 2 & 3 (test)

RoBERTa for RLHF Stage 2 & 3 (still in testing)

* Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"

This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368.

* Add RoBERTa for RLHF stage 2 & 3

1. add roberta folder under model folder
2. add  roberta option in train_reward_model.py
3. add some test in testci

* add test for reward model training

* Update test_ci.sh

* Revert "Update test_ci.sh"

This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a.

* Add RoBERTa for RLHF Stage 2 & 3 (test)

RoBERTa for RLHF Stage 2 & 3 (still in testing)

* Revert "Add RoBERTa for RLHF Stage 2 & 3 (test)"

This reverts commit 06741d894dcbe958acd4e10d771f22275e20e368.

* Add RoBERTa for RLHF stage 2 & 3

1. add roberta folder under model folder
2. add  roberta option in train_reward_model.py
3. add some test in testci

* Update test_ci.sh

* Revert "Update test_ci.sh"

This reverts commit 9c7352b81766f3177d31eeec0ec178a301df966a.

* update roberta with coati