Welcome to the world of Prompt Tuning—an innovative approach that emphasizes parameter-efficient tuning in large language models. In this guide, we’ll walk you through the essential steps to implement prompt tuning based on the EMNLP 2021 paper “The Power of Scale for Parameter-Efficient Prompt Tuning” by Lester et al.

Table of Contents

• Installation
• Training a Prompt
• Inference with a Prompt
• Model Configuration
• Prompt Initialization
• Released Model Checkpoints
• Released Prompts
• Extra Resources

Installation

Before we dive into training prompts, we must install the necessary frameworks. Here’s how to set it all up:

1. Follow the first three steps in the T5X installation instructions to create a cloud TPU VM. Make sure to create a Google Cloud Storage (GCS) bucket to store your datasets and model checkpoints.
2. Clone the Prompt Tuning repository:

git clone --branch=main https://github.com/google-research/prompt-tuning
cd prompt-tuning

3. Install the Prompt Tuning library:

python3 -m pip install .[tpu] -f https://storage.googleapis.com/jax-releases/lib/tpu_releases.html

Training a Prompt

Think of training a prompt as fine-tuning a model, but instead, you focus specifically on adjusting the prompts used with it. The process is straightforward:

1. Execute the demo script:

bash prompt_tuning/scripts/sst2-demo.sh

To enhance iteration speed, you may want to specify multiple options in your command line, rather than all in a single configuration file. Some options to consider:

• --gin_search_paths: Path prefixes for gin files.
• --gin_file: The gin file to load.
• --gin.PARAM=VALUE: General override flag to set configuration options.

Inference with a Prompt

For inference, load the original checkpoint and the prompt file:

python3 -m your_inference_script --checkpoint_path  --prompt_file 

This combination leverages T5X’s support for loading some parameters while initializing others from scratch.

Model Configuration

Configuration in T5X is managed through gin. The configuration layout includes:

• runs: Contains configs for actual training, dataset, etc.
• architectures: Configures how the model works.
• models: Sets parameter-specific values.

Prompt Initialization

Initializing prompts can be done via different methods:

• Random Uniform: Standard random initialization.
• Sampled Vocab: Sample a token embedding for initialization.
• From String: Load a prompt from a string representation.

Released Model Checkpoints

You can access various T5X native checkpoints with different parameters, like the T5 1.1 model. Here are some examples:

• T5 1.1 Small (~77 million parameters)
• T5 1.1 Base (~250 million parameters)

Released Prompts

Explore our variety of pretrained prompts located in the pretrained_prompts directory.

Extra Resources

Want to dive deeper? Check out these resources:

• EMNLP Presentation Video
• EMNLP Presentation Slides

Troubleshooting Tips

If you encounter issues during installation or execution, here are some quick troubleshooting tips:

• If you experience errors with dependency installations, try adding --use-deprecated=legacy-resolver to your pip command.
• Make sure to monitor for warnings about version mismatches during installations.
• If a training script fails, ensure that all processes using TPUs are terminated before restarting your job by running:

gcloud alpha compute tpus tpu-vm ssh $TPU_NAME --zone $ZONE --worker=all --command=sudo lsof -t /dev/accel0 | xargs kill -9

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