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Pre-Training CPT

The code of pre-training CPT is based on Megatron-LM.

For Setup, Data Processing of CPT, you can refer to the README of Megatron-LM. And the package jieba_fast is needed for Whole Word Masking pre-training.

Training

Firstly, prepare files in the following folders：

  • dataset/: Place the .bin and .idx files that preprocessed from raw text.
  • vocab/: Place the vocab files and model config file.
  • roberta_zh/: Place the checkpoint of Chinese RoBERTa, as the CPT initialize the encoder from the checkpoint.

Then, use the scripts run_pretrain_bart.sh and run_pretrain_cpt.sh to train Chinese BART and CPT, respectively.

NOTE: the training scripts is distributed examples for 8 GPUs. You may alter the number of GPUs and change the training steps to meet the need.

Main Changes

  • Add bart_model and cpt_model for Megatron under megatron/model, to let Megatron can train on BART and CPT.
  • Add _HfBertTokenizer in megatron/tokenizer/tokenizer.py to let Megatron can use Tokenizers from Huggingface-Transformers.
  • Add bart_dataset and cpt_dataset under megatron/data to produce data for Whole Word Masking (WWM) and Denoising Auto-Encoder (DAE) pre-training.
  • Add tools/convert_ckpt.py to convert Megatron checkpoints to Huggingface-Transformers format.
  • Add tools/preprocess_data.py to preprocess and chunk large amount of text data into binary format used in Megatron.