6d41c3f2aa
[doc] update Coati README ( #4405 )
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* style: apply formatter
* fix: add outdated warnings
* docs: add dataset format and polish
* docs: polish README
* fix: fix json format
* fix: fix typos
* revert: revert 7b example
2023-08-14 15:26:27 +08:00
da4f7b855f
[chat] fix bugs and add unit tests ( #4213 )
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* style: rename replay buffer
Experience replay is typically for off policy algorithms.
Use this name in PPO maybe misleading.
* fix: fix wrong zero2 default arg
* test: update experience tests
* style: rename zero_pad fn
* fix: defer init in CycledDataLoader
* test: add benchmark test
* style: rename internal fn of generation
* style: rename internal fn of lora
* fix: remove unused loss fn
* fix: remove unused utils fn
* refactor: remove generate_with_actor fn
* fix: fix type annotation
* test: add models tests
* fix: skip llama due to long execution time
* style: modify dataset
* style: apply formatter
* perf: update reward dataset
* fix: fix wrong IGNORE_INDEX in sft dataset
* fix: remove DataCollatorForSupervisedDataset
* test: add dataset tests
* style: apply formatter
* style: rename test_ci to test_train
* feat: add llama in inference
* test: add inference tests
* test: change test scripts directory
* fix: update ci
* fix: fix typo
* fix: skip llama due to oom
* fix: fix file mod
* style: apply formatter
* refactor: remove duplicated llama_gptq
* style: apply formatter
* to: update rm test
* feat: add tokenizer arg
* feat: add download model script
* test: update train tests
* fix: modify gemini load and save pretrained
* test: update checkpoint io test
* to: modify nproc_per_node
* fix: do not remove existing dir
* fix: modify save path
* test: add random choice
* fix: fix sft path
* fix: enlarge nproc_per_node to avoid oom
* fix: add num_retry
* fix: make lora config of rm and critic consistent
* fix: add warning about lora weights
* fix: skip some gpt2 tests
* fix: remove grad ckpt in rm and critic due to errors
* refactor: directly use Actor in train_sft
* test: add more arguments
* fix: disable grad ckpt when using lora
* fix: fix save_pretrained and related tests
* test: enable zero2 tests
* revert: remove useless fn
* style: polish code
* test: modify test args
2023-08-02 10:17:36 +08:00
3d8d5d0d58
[chat] use official transformers and fix some issues ( #4117 )
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* 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
2023-07-04 13:49:09 +08:00
edd75a59ea
[chat] remove naive strategy and split colossalai strategy ( #4094 )
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* 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
2023-06-29 18:11:00 +08:00
d4fb7bfda7
fix typo applications/Chat/coati/ ( #3947 )
2023-06-15 10:43:11 +08:00
b5f0566363
[chat] add distributed PPO trainer ( #3740 )
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* 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
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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
---------
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>
2023-06-07 10:41:16 +08:00
e2d81eba0d
[nfc] fix typo colossalai/ applications/ ( #3831 )
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* 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/
2023-05-25 16:19:41 +08:00
d7bf284706
[chat] polish code note typo ( #3612 )
2023-04-20 17:22:15 +08:00
e355144375
[chatgpt] Detached PPO Training ( #3195 )
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* 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>
2023-04-17 14:46:50 +08:00
Wenhao Chen