Inference with Gemma using JAX and Flax

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Overview

Gemma is a family of lightweight, state-of-the-art open large language models, based on the Google DeepMind Gemini research and technology. This tutorial demonstrates how to perform basic sampling/inference with the Gemma 2B Instruct model using Google DeepMind's gemma library that was written with JAX (a high-performance numerical computing library), Flax (the JAX-based neural network library), Orbax (a JAX-based library for training utilities like checkpointing), and SentencePiece (a tokenizer/detokenizer library). Although Flax is not used directly in this notebook, Flax was used to create Gemma.

This notebook can run on Google Colab with free T4 GPU (go to Edit > Notebook settings > Under Hardware accelerator select T4 GPU).

Setup

1. Set up Kaggle access for Gemma

To complete this tutorial, you first need to follow the setup instructions at Gemma setup, which show you how to do the following:

  • Get access to Gemma on kaggle.com.
  • Select a Colab runtime with sufficient resources to run the Gemma model.
  • Generate and configure a Kaggle username and API key.

After you've completed the Gemma setup, move on to the next section, where you'll set environment variables for your Colab environment.

2. Set environment variables

Set environment variables for KAGGLE_USERNAME and KAGGLE_KEY. When prompted with the "Grant access?" messages, agree to provide secret access.

import os
from google.colab import userdata # `userdata` is a Colab API.

os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')

3. Install the gemma library

This notebook focuses on using a free Colab GPU. To enable hardware acceleration, click on Edit > Notebook settings > Select T4 GPU > Save.

Next, you need to install the Google DeepMind gemma library from github.com/google-deepmind/gemma. If you get an error about "pip's dependency resolver", you can usually ignore it.

pip install -q git+https://github.com/google-deepmind/gemma.git

Load and prepare the Gemma model

  1. Load the Gemma model with kagglehub.model_download, which takes three arguments:
  • handle: The model handle from Kaggle
  • path: (Optional string) The local path
  • force_download: (Optional boolean) Forces to re-download the model
GEMMA_VARIANT = 'gemma2-2b-it' # @param ['gemma2-2b', 'gemma2-2b-it'] {type:"string"}

import kagglehub

GEMMA_PATH = kagglehub.model_download(f'google/gemma-2/flax/{GEMMA_VARIANT}')

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 96%|█████████▌| 2.03G/2.12G [00:33<00:01, 91.6MB/s]
 96%|█████████▋| 2.04G/2.12G [00:33<00:00, 96.2MB/s]
 97%|█████████▋| 2.05G/2.12G [00:33<00:00, 108MB/s] 
 98%|█████████▊| 2.06G/2.12G [00:33<00:00, 89.9MB/s]
 98%|█████████▊| 2.08G/2.12G [00:34<00:00, 103MB/s] 
 99%|█████████▉| 2.09G/2.12G [00:34<00:00, 115MB/s]
100%|██████████| 2.12G/2.12G [00:34<00:00, 66.0MB/s]

print('GEMMA_PATH:', GEMMA_PATH)

GEMMA_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1
  1. Check the location of the model weights and the tokenizer, then set the path variables. The tokenizer directory will be in the main directory where you downloaded the model, while the model weights will be in a sub-directory. For example:
  • The tokenizer.model file will be in /LOCAL/PATH/TO/gemma/flax/2b-it/2).
  • The model checkpoint will be in /LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it).
CKPT_PATH = os.path.join(GEMMA_PATH, GEMMA_VARIANT)
TOKENIZER_PATH = os.path.join(GEMMA_PATH, 'tokenizer.model')
print('CKPT_PATH:', CKPT_PATH)
print('TOKENIZER_PATH:', TOKENIZER_PATH)

CKPT_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/gemma2-2b-it
TOKENIZER_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/tokenizer.model

Perform sampling/inference

  1. Load and format the Gemma model checkpoint with the gemma.params.load_and_format_params method:
from gemma import params as params_lib

params = params_lib.load_and_format_params(CKPT_PATH)
  1. Load the Gemma tokenizer, constructed using sentencepiece.SentencePieceProcessor:
import sentencepiece as spm

vocab = spm.SentencePieceProcessor()
vocab.Load(TOKENIZER_PATH)

True
  1. To automatically load the correct configuration from the Gemma model checkpoint, use gemma.transformer.TransformerConfig. The cache_size argument is the number of time steps in the Gemma Transformer cache. Afterwards, instantiate the Gemma model as transformer with gemma.transformer.Transformer (which inherits from flax.linen.Module).
from gemma import transformer as transformer_lib

transformer_config = transformer_lib.TransformerConfig.from_params(
    params=params,
    cache_size=1024
)

transformer = transformer_lib.Transformer(transformer_config)
  1. Create a sampler with gemma.sampler.Sampler on top of the Gemma model checkpoint/weights and the tokenizer:
from gemma import sampler as sampler_lib

sampler = sampler_lib.Sampler(
    transformer=transformer,
    vocab=vocab,
    params=params['transformer'],
)
  1. Write a prompt in input_batch and perform inference. You can tweak total_generation_steps (the number of steps performed when generating a response — this example uses 100 to preserve host memory).
prompt = [
    "what is JAX in 3 bullet points?",
]

reply = sampler(input_strings=prompt,
                total_generation_steps=128,
                )

for input_string, out_string in zip(prompt, reply.text):
    print(f"Prompt:\n{input_string}\nOutput:\n{out_string}")

Prompt:
what is JAX in 3 bullet points?
Output:


* **High-performance numerical computation:** JAX leverages the power of GPUs and TPUs to accelerate complex mathematical operations, making it ideal for scientific computing, machine learning, and data analysis.
* **Automatic differentiation:** JAX provides automatic differentiation capabilities, allowing you to compute gradients and optimize models efficiently. This simplifies the process of training deep learning models.
* **Functional programming:** JAX embraces functional programming principles, promoting code readability and maintainability. It offers a flexible and expressive syntax for defining and manipulating data. 


<end_of_turn>
  1. (Optional) Run this cell to free up memory if you have completed the notebook and want to try another prompt. Afterwards, you can instantiate the sampler again in step 3 and customize and run the prompt in step 4.
del sampler

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