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Continued Pretraining

This directory contains the code for continued pretraining.

Installation

Create a conda environment:

conda create -n trainenv python=3.10

Activate the environment:

conda activate trainenv

Install torch from PyTorch.

Install the required packages:

pip install -r train/requirements.txt

Training

Step 1: save the paths of the jsonl files to be tokenized

Modify in_dirs in scripts/get_files_to-be-tokenized.py. Exch element of in_dirs should be the path to a directory that contains .jsonl files with the texts to be trainined under the key "text". Then run:

python train/scripts/get_files_to-be-tokenized.py

Step 2: tokenize the files

You can modify out_dir in scripts/tokenize_data.py to change the output directory. Then run:

python train/scripts/tokenize_data.py

Step 3: save the paths of the tokenized parquet files

You can modify in_dirs in scripts/get_files_to-be-trained.py to change the paths to the directories that contains the tokenized parquet files. Each element should be a path to a directory that contains parquet files created in Step 2. Then run:

python train/scripts/get_files_to-be-trained.py

Step 4: group the texts into given context length

You can modify max_len in scripts/group_text.py to change the context length. This context length should equal the context length you wish to use in training. Then run:

python train/scripts/group_text.py

This outputs a single parquet file containing token indexes grouped into the given context length.

Step 5: train the model

You can modify the training configs in scripts/train.py. The example script should be run on a cluster of 8 nodes with 4 A800 GPUs on each node. The cluster we used adpots an All Reduce-DDP structure, with $WORLD_SIZE, $RANK and $MASTER_PORT automatically configured. You may need to modify the script so that it runs on your cluster. Run:

bash train/scripts/train.sh

This script trains the model for 3 epochs with a batch size of 512 (8 node x 4 gpu_per_node x 4 per_device_train_batch_size x 4 gradient_accumulation_steps).