GitLab

* Add dpo. Fix sft, ppo, lora. Refactor all

* fix and tested ppo

* 2 nd round refactor

* add ci tests

* fix ci

* fix ci

* fix readme, style

* fix readme style

* fix style, fix benchmark

* reproduce benchmark result, remove useless files

* rename to ColossalChat

* use new image

* fix ci workflow

* fix ci

* use local model/tokenizer for ci tests

* fix ci

* fix ci

* fix ci

* fix ci timeout

* fix rm progress bar. fix ci timeout

* fix ci

* fix ci typo

* remove 3d plugin from ci temporary

* test environment

* cannot save optimizer

* support chat template

* fix readme

* fix path

* test ci locally

* restore build_or_pr

* fix ci data path

* fix benchmark

* fix ci, move ci tests to 3080, disable fast tokenizer

* move ci to 85

* support flash attention 2

* add all-in-one data preparation script. Fix colossal-llama2-chat chat template

* add hardware requirements

* move ci test data

* fix save_model, add unwrap

* fix missing bos

* fix missing bos; support grad accumulation with gemini

* fix ci

* fix ci

* fix ci

* fix llama2 chat template config

* debug sft

* debug sft

* fix colossalai version requirement

* fix ci

* add sanity check to prevent NaN loss

* fix requirements

* add dummy data generation script

* add dummy data generation script

* add dummy data generation script

* add dummy data generation script

* update readme

* update readme

* update readme and ignore

* fix logger bug

* support parallel_output

* modify data preparation logic

* fix tokenization

* update lr

* fix inference

* run pre-commit

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Co-authored-by: Tong Li <tong.li352711588@gmail.com>

* [chat] fix gemini strategy

* [chat] fix gemini strategy

* [chat] fix gemini strategy

* [chat] fix gemini strategy

* g# This is a combination of 2 commits.

[chat] fix gemini strategy

fox

* [chat] fix gemini strategy

update llama2 example

[chat] fix gemini strategy

* [fix] fix gemini strategy

* [fix] fix gemini strategy

* [fix] fix gemini strategy

* [fix] fix gemini strategy

* [fix] fix gemini strategy

* [fix] fix gemini strategy

* [fix] fix gemini strategy

* [fix] fix gemini strategy

* [fix] fix gemini strategy

* [fix] fix gemini strategy

* fix

* fix

* fix

* fix

* fix

* Update train_prompts.py

* feat: modify forward fn of critic and reward model

* feat: modify calc_action_log_probs

* to: add wandb in sft and rm trainer

* feat: update train_sft

* feat: update train_rm

* style: modify type annotation and add warning

* feat: pass tokenizer to ppo trainer

* to: modify trainer base and maker base

* feat: add wandb in ppo trainer

* feat: pass tokenizer to generate

* test: update generate fn tests

* test: update train tests

* fix: remove action_mask

* feat: remove unused code

* fix: fix wrong ignore_index

* fix: fix mock tokenizer

* chore: update requirements

* revert: modify make_experience

* fix: fix inference

* fix: add padding side

* style: modify _on_learn_batch_end

* test: use mock tokenizer

* fix: use bf16 to avoid overflow

* fix: fix workflow

* [chat] fix gemini strategy

* [chat] fix

* sync: update colossalai strategy

* fix: fix args and model dtype

* fix: fix checkpoint test

* fix: fix requirements

* fix: fix missing import and wrong arg

* fix: temporarily skip gemini test in stage 3

* style: apply pre-commit

* fix: temporarily skip gemini test in stage 1&2

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Co-authored-by: Mingyan Jiang <1829166702@qq.com>

* [misc] update pre-commit

* [misc] run pre-commit

* [misc] remove useless configuration files

* [misc] ignore cuda for clang-format

Co-authored-by: flybird11111 <1829166702@qq.com>

* feat: remove on_learn_epoch fn as not used

* revert: add _on_learn_epoch fn

* feat: remove NaiveStrategy

* test: update train_prompts tests

* fix: remove prepare_llama_tokenizer_and_embedding

* test: add lora arg

* feat: remove roberta support in train_prompts due to runtime errs

* feat: remove deberta & roberta in rm as not used

* test: remove deberta and roberta tests

* feat: remove deberta and roberta models as not used

* fix: remove calls to roberta

* fix: remove prepare_llama_tokenizer_and_embedding

* chore: update transformers version

* docs: update transformers version

* fix: fix actor inference

* fix: fix ci

* feat: change llama pad token to unk

* revert: revert ddp setup_distributed

* fix: change llama pad token to unk

* revert: undo unnecessary changes

* fix: use pip to install transformers

* feat: remove on_learn_epoch fn as not used

* revert: add _on_learn_epoch fn

* to: remove the use of NaiveStrategy

* test: remove NaiveStrategy tests

* feat: remove NaiveStrategy

* style: modify comments and params

* feat: split ColossalAIStrategy into LowLevelZeroStrategy and GeminiStrategy

* fix: remove naive

* fix: align with modified colossal strategy

* fix: fix ddp _try_init_dist arg

* 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

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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

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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

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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

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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

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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

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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

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