This directory includes GPT-3/BERT pretraining example scripts for DeepSpeed Data Efficiency Library technologies (curriculum learning, random-LTD, and the two composed together).
You need to install updated DeepSpeed version (>=0.8.0), which contains the DeepSpeed Data Efficiency Library.
Additional tutorial can be found at DeepSpeed website.
Additional technical details can be found in our random-LTD paper and data efficiency paper.
GPT-3 pretraining and evaluation
Inside gpt folder, first the ds_analyze_gpt_data_map.sh and ds_analyze_gpt_data_reduce.sh are used for curriculum learning's offline data analysis and indexing.
gpt/pretrain includes the pretraining example scripts. You can choose a setup to run by uncommenting one block in ds_pretrain_gpt_1.3B_dense_run.sh. One thing to note is that in our random-LTD paper we did not scale peak learning rate when using less than 100% data, while in our later data efficiency paper we find that scaling LR based on used percentage of data helps improve model quality.
gpt/eval includes the zero-/few-shot evaluation example scripts. ds_evalharness_parallel_run.sh is for zero-shot, and ds_evalharness_parallel_run_10shot.sh is for 10-shot.
BERT pretraining and finetuning
Inside bert folder, first the pile_data_download_preprocess.py can be used to download and preprocess the public Pile dataset.
The ds_analyze_bert_data_map.sh and ds_analyze_bert_data_reduce.sh are used for curriculum learning's offline data analysis and indexing.
bert/pretrain includes the pretraining example scripts. You can choose a setup to run by uncommenting one block in ds_pretrain_bert_336M_run.sh. One thing to note is that in our random-LTD paper we did not scale peak learning rate when using less than 100% data, while in our later data efficiency paper we find that scaling LR based on used percentage of data helps improve model quality.
bert/finetune includes the MNLI/QQP/RACE finetuning example scripts following the Megatron-LM paper. However, we found that the RACE task's accuracy is not very stable and the Megatron-LM paper used a very long number of epochs for MNLI/QQP which is not necessary. Thus we added capability of finetuning other GLUE tasks, and switched to follow the hyperparameters of the original BERT paper. The corresponding scripts are at bert/finetune_glue, which we recommend to use instead of bert/finetune. Our data efficiency paper also uses the scripts under bert/finetune_glue for GLUE finetuning.