Finetune BERT on GLUE

🚀 Quick Start

This example provides a training script, which provides an example of finetuning BERT on GLUE dataset.

Training Arguments
- -t, --task: GLUE task to run. Defaults to mrpc.
- -p, --plugin: Plugin to use. Choices: torch_ddp, torch_ddp_fp16, gemini, low_level_zero. Defaults to torch_ddp.
- --target_f1: Target f1 score. Raise exception if not reached. Defaults to None.

Install requirements

bash

pip install -r requirements.txt

Train

bash

# train with torch DDP with fp32
colossalai run --nproc_per_node 4 finetune.py

# train with torch DDP with mixed precision training
colossalai run --nproc_per_node 4 finetune.py -p torch_ddp_fp16

# train with gemini
colossalai run --nproc_per_node 4 finetune.py -p gemini

# train with low level zero
colossalai run --nproc_per_node 4 finetune.py -p low_level_zero

Expected F1-score will be:

Model	Single-GPU Baseline FP32	Booster DDP with FP32	Booster DDP with FP16	Booster Gemini	Booster Low Level Zero
bert-base-uncased	0.86	0.88	0.87	0.88	0.89