This is not an officially supported Google product.
This is the source code for the paper "Amos: An Adam-style Optimizer with Adaptive Weight Decay towards Model-Oriented Scale".
It implements Amos, an optimizer compatible with the optax library, and JEstimator, a light-weight library with a tf.Estimator-like interface to manage T5X-compatible checkpoints for machine learning programs in Jax, which we use to run experiments in the paper.
In order to run a test for Amos, we need to install Abseil, Jax, Flax and Chex:
pip install absl-py # Install Abseil
pip install --upgrade pip
pip install --upgrade "jax[cpu]" # Install Jax
pip install flax # Install Flax
pip install chex # Install Chex
Then, checkout the repository and run the test:
git clone --branch=main https://github.com/google-research/jestimator
PYTHONPATH=. python3 jestimator/amos_test.py
The data pipeline of JEstimator is built on Tensorflow, although in principle it can be replaced by PyTorch DataLoader as well. We also need the T5X and FlaxFormer library.
pip install tensorflow-cpu # Install Tensorflow
git clone --branch=main https://github.com/google-research/t5x
cd t5x # Install T5X with TPU support, so we can pre-train on Google Cloud:
python3 -m pip install -e '.[tpu]' -f \
https://storage.googleapis.com/jax-releases/libtpu_releases.html
cd ..
git clone --branch=main https://github.com/google/flaxformer
cd flaxformer # Install FlaxFormer:
pip3 install '.[testing]'
cd ..
Then, we can test a toy linear regression model with JEstimator:
JAX_PLATFORMS=cpu PYTHONPATH=. python3 jestimator/models/linear_regression/linear_regression_test.py
And we can train a single layer LSTM model on PTB:
JAX_PLATFORMS=cpu PYTHONPATH=. python3 jestimator/estimator.py \
--module_imp="jestimator.models.lstm.lm" \
--module_config="jestimator/models/lstm/lm.py" \
--module_config.vocab_path="jestimator/models/lstm/ptb/vocab.txt" \
--train_pattern="jestimator/models/lstm/ptb/ptb.train.txt" \
--model_dir="$HOME/models/ptb_lstm" \
--train_batch_size=64 \
--train_consecutive=113 \
--train_shuffle_buf=4096 \
--max_train_steps=200000 \
--check_every_steps=1000 \
--max_ckpt=20 \
--module_config.opt_config.optimizer="amos" \
--module_config.opt_config.learning_rate=0.01 \
--module_config.opt_config.beta=0.98 \
--module_config.opt_config.momentum=0.0 \
--logtostderr
After the training completes, we can evaluate the model on validation set:
JAX_PLATFORMS=cpu PYTHONPATH=. python3 jestimator/estimator.py \
--module_imp="jestimator.models.lstm.lm" \
--module_config="jestimator/models/lstm/lm.py" \
--module_config.vocab_path="jestimator/models/lstm/ptb/vocab.txt" \
--eval_pattern="jestimator/models/lstm/ptb/ptb.valid.txt" \
--model_dir="$HOME/models/ptb_lstm" \
--eval_batch_size=1 \
--logtostderr