VinBERT

VinBERT is a combination of two powerful Vietnamese language models: Vintern-1b-v2 and PhoBERT. With VinBERT, we create a language model optimized to better serve applications in the Vietnamese language, including tasks such as text classification, entity extraction, and more.

Objectives

• VinBERT leverages the strengths of Vintern-1b-v2 and PhoBERT, providing high efficiency and accuracy for Vietnamese NLP applications.
• It supports distributed training on multiple GPUs and AWS Sagemaker infrastructure, optimizing time and resources.

Support training

• cuda: Data parallelism and Model parallelism are supported with backend nccl
• xla : Data parallelism are supported with backend xla

Requirements

• An AWS account with access to Sagemaker.
• An environment set up to interact with AWS CLI and Sagemaker.
• You have quota to use ml.p4d.24xlarge and ml.trn1.32xlarge instances.

    pip install -r requirements.txt

Distributed Training on GPU (AWS Sagemaker ml.p4d.24xlarge)

1. Prepare the environment: pull docker image flash attn base from dockerhub: vantufit/flash-attn-cuda

docker pull vantufit/flash-attn-cuda

2. Run the job:
   • Configure parameters such as instance type, number of GPUs, and batch size.
   • Run the following command to initiate the job:

   export INSTANCE=ml.p4d.24xlarge
   python training.py

Training with Trainium (ml.trn1.32xlarge)

1. Run the job:

   export INSTANCE=ml.trn1.32xlarge
   python training.py

TODO: Monitoring Training

• Implement Tensor parallelism with neuronx_distributed

• Monitoring training process