Add whiten ops before compute advatanges#887
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vwxyzjn merged 2 commits intohuggingface:mainfrom Oct 23, 2023
SingL3:main
Merged
Add whiten ops before compute advatanges#887vwxyzjn merged 2 commits intohuggingface:mainfrom SingL3:main
vwxyzjn merged 2 commits intohuggingface:mainfrom
SingL3:main
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SingL3
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Oct 18, 2023
SingL3
commented
- From LLaMA 2 paper, it says:
- This function is taken from alpaca_farm
added 2 commits
October 18, 2023 12:00
1. From LLaMA 2 paper, it says: ``` We also find it important to whiten the final linear scores (shown here by reversing the sigmoid with the logit function) in order to increase stability and balance properly with the KL penalty term (β) above. ``` 2. This function is taken from [alpaca_farm](https://github.com/tatsu-lab/alpaca_farm/blob/64e489c67ea502ab5fa944bebde3078c9722f6ee/src/alpaca_farm/rl/ppo_trainer.py#L86)
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Contributor
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@SingL3, thanks for the change! It makes sense. I have kicked started our benchmark to help understand its effect 
|
vwxyzjn
approved these changes
Oct 19, 2023
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No noticeable difference in performance with whiten_rewards=True. We can even the set the default value to True. WDYT @lvwerra?
**Plot commands:**
# pip install openrlbenchmark==0.2.1a5
# see https://github.com/openrlbenchmark/openrlbenchmark#get-started for documentation
export TAGS_STRING='?tag=v0.4.7-137-g3f90b24&tag=pr-887'
export FOLDER_STRING='v0.4.7-137-g3f90b24_pr-887'
echo "we deal with $TAGS_STRING"
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo$TAGS_STRING" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/hello_world \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo$TAGS_STRING" \
"ppo_gpt2xl_grad_accu$TAGS_STRING" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/different_models \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo_Cerebras-GPT-6.7B_grad_accu_deepspeed_stage2$TAGS_STRING" \
--env-ids sentiment-analysis:cerebras/Cerebras-GPT-6.7B \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/deepspeed \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo$TAGS_STRING" \
"ppo_step_grad_accu$TAGS_STRING" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/grad_accu \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo$TAGS_STRING" \
"ppo_gpt2$TAGS_STRING" \
"ppo_falcon_rw_1b$TAGS_STRING" \
"ppo_peft$TAGS_STRING" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/more_different_models \
--scan-history
python benchmark/upload_benchmark.py \
--folder_path="benchmark/trl/$FOLDER_STRING" \
--path_in_repo="images/benchmark/$FOLDER_STRING" \
--repo_id="trl-internal-testing/example-images" \
--repo_type="dataset"
# pip install openrlbenchmark==0.2.1a5
# see https://github.com/openrlbenchmark/openrlbenchmark#get-started for documentation
TAGS_STRING='?tag=v0.4.7-137-g3f90b24&tag=pr-887'
FOLDER_STRING='pr-887_vs_baseline'
BASELINE_PR_TAG=v0.4.7-55-g110e672
BASELINE_PR_NAME=PR-662
echo "we deal with $TAGS_STRING"
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo$TAGS_STRING" \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"sentiment_tuning?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb ($BASELINE_PR_NAME)" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/hello_world \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo$TAGS_STRING" \
"ppo_gpt2xl_grad_accu$TAGS_STRING" \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"sentiment_tuning?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb ($BASELINE_PR_NAME)" \
"sentiment_tuning_gpt2xl_grad_accu?tag=$BASELINE_PR_TAG&cl=sentiment gpt2xl ($BASELINE_PR_NAME)" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/different_models \
--scan-history
python -m openrlbenchmark.rlops_multi_metrics \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"ppo$TAGS_STRING" \
"ppo_gpt2$TAGS_STRING" \
"ppo_falcon_rw_1b$TAGS_STRING" \
"ppo_peft$TAGS_STRING" \
--filters '?we=huggingface&wpn=trl&xaxis=_step&ceik=trl_ppo_trainer_config.value.reward_model&cen=trl_ppo_trainer_config.value.exp_name&metrics=env/reward_mean&metrics=objective/kl' \
"sentiment_tuning?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb ($BASELINE_PR_NAME)" \
"sentiment_tuning_gpt2?tag=$BASELINE_PR_TAG&cl=sentiment gpt2 ($BASELINE_PR_NAME)" \
"sentiment_tuning_falcon_rw_1b?tag=$BASELINE_PR_TAG&cl=sentiment tiiuae/falcon-rw-1b ($BASELINE_PR_NAME)" \
"sentiment_tuning_peft?tag=$BASELINE_PR_TAG&cl=sentiment lvwerra/gpt2-imdb w/ peft ($BASELINE_PR_NAME)" \
--env-ids sentiment-analysis:lvwerra/distilbert-imdb \
--no-check-empty-runs \
--pc.ncols 2 \
--pc.ncols-legend 1 \
--output-filename benchmark/trl/$FOLDER_STRING/more_different_models \
--scan-history
python benchmark/upload_benchmark.py \
--folder_path="benchmark/trl/$FOLDER_STRING" \
--path_in_repo="images/benchmark/$FOLDER_STRING" \
--repo_id="trl-internal-testing/example-images" \
--repo_type="dataset"
Contributor
|
I am going to merge as is. Thanks so much @SingL3! |
kashif
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to kashif/trl
that referenced
this pull request
Oct 27, 2023
* Add whiten ops before compute advatanges 1. From LLaMA 2 paper, it says: ``` We also find it important to whiten the final linear scores (shown here by reversing the sigmoid with the logit function) in order to increase stability and balance properly with the KL penalty term (β) above. ``` 2. This function is taken from [alpaca_farm](https://github.com/tatsu-lab/alpaca_farm/blob/64e489c67ea502ab5fa944bebde3078c9722f6ee/src/alpaca_farm/rl/ppo_trainer.py#L86) * Fix type def of self --------- Co-authored-by: Lin Junpeng <linjunpeng@sensetime.com>
lapp0
pushed a commit
to lapp0/trl
that referenced
this pull request
May 10, 2024
* Add whiten ops before compute advatanges 1. From LLaMA 2 paper, it says: ``` We also find it important to whiten the final linear scores (shown here by reversing the sigmoid with the logit function) in order to increase stability and balance properly with the KL penalty term (β) above. ``` 2. This function is taken from [alpaca_farm](https://github.com/tatsu-lab/alpaca_farm/blob/64e489c67ea502ab5fa944bebde3078c9722f6ee/src/alpaca_farm/rl/ppo_trainer.py#L86) * Fix type def of self --------- Co-authored-by: Lin Junpeng <linjunpeng@sensetime.com>
yxliu-TAMU
pushed a commit
to mincheolseong/ECEN743-GRPO-Project-Proposal
that referenced
this pull request
Apr 20, 2025
* Add whiten ops before compute advatanges 1. From LLaMA 2 paper, it says: ``` We also find it important to whiten the final linear scores (shown here by reversing the sigmoid with the logit function) in order to increase stability and balance properly with the KL penalty term (β) above. ``` 2. This function is taken from [alpaca_farm](https://github.com/tatsu-lab/alpaca_farm/blob/64e489c67ea502ab5fa944bebde3078c9722f6ee/src/alpaca_farm/rl/ppo_trainer.py#L86) * Fix type def of self --------- Co-authored-by: Lin Junpeng <linjunpeng@sensetime.com>
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