* update configuration of chatglm and add support in coati
* add unit test & update chatglm default config & fix bos index issue
* remove chatglm due to oom
* add dataset pkg in requirement-text
* fix parameter issue in test_models
* add ref in tokenize & rm unnessary parts
* separate source & target tokenization in chatglm
* add unit test to chatglm
* fix test dataset issue
* update truncation of chatglm
* fix Colossalai version
* fix colossal ai version in test
* 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
* refactor: adapt boost API in base and naive strategies
* fix: initialize plugin after setup_distributed
* fix: fix save_pretrained fn
* refactor: adapt boost API in DDPStrategy
* to: add _post_init check
* to: fix ddp backward, modify ddp dataloader and unwrap
* feat: adapt boost API in ColossalAIStrategy
* fix: call setup_distributed before use get_current_device
* fix: fix save_model and save_optimizer
* test: remove save_sharded_optimizer test
* style: apply formatter
* fix: fix stage check and add comments
* feat: allow dict type arg in strategy.prepare
* to: temporarily remove lr_scheduler for testing
* style: simplify init of ColossalAIStrategy
* fix: fix lr_scheduler in sft and rm
* style: modify comments
* test: add train_prompts tests
* fix: fix inference only case and use in train_prompts
* test: skip failed tests in ci
* style: fix CodeFactor check
* fix: do not use model.to('cpu') with GeminiPlugin
* test: enable colossalai_gemini tests
* test: set CUDA_VISIBLE_DEVICES in ci
* docs: add note
* refactor: separate log_probs fn from Actor forward fn
* refactor: separate generate fn from Actor class
* feat: update unwrap_model and get_base_model
* unwrap_model returns model not wrapped by Strategy
* get_base_model returns HF model for Actor, Critic and RewardModel
* feat: simplify Strategy.prepare
* style: remove get_base_model method of Actor
* perf: tokenize text in batches
* refactor: move calc_action_log_probs to utils of model
* test: update test with new forward fn
* style: rename forward fn args
* fix: do not unwrap model in save_model fn of naive strategy
* test: add gemini test for train_prompts
* fix: fix _set_default_generate_kwargs
* [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
* 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>
* 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
* 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 06741d894d.
* 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 06741d894d.
* 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
Hongxin Liu