• Link To The Blog Post

Experiments

MNIST Sequence Modeling

python -m s4.train --dataset mnist --model s4 --epochs 100 --bsz 128 --d_model 128 --ssm_n 64

QuickDraw Sequence Modeling

# Default arguments
python -m s4.train --dataset quickdraw --model s4 --epochs 10 --bsz 128 --d_model 128 --ssm_n 64

# "Run in a day" variant
python -m s4.train --dataset quickdraw --model s4 --epochs 1 --bsz 512 --d_model 256 --ssm_n 64 --p_dropout 0.05

MNIST Classification

python -m s4.train --dataset mnist-classification --model s4 --epochs 10 --bsz 128 --d_model 128 --ssm_n 64

Gets "best" 97.76% accuracy in 10 epochs @ 40s/epoch on a TitanRTX.

CIFAR-10 Classification

# Following @frederick0329's/@albertgu's results: https://github.com/srush/annotated-s4/pull/43#issuecomment-1065444261
python -m s4.train --dataset cifar-classification --model s4 --epoch 100 --bsz 64 --n_layers 6 --p_dropout 0.25 --lr 5e-3 --d_model 512

# DSS Model
python -m s4.train --dataset cifar-classification --model dss --epoch 100 --bsz 64 --n_layers 6 --p_dropout 0.25 --lr 5e-3 --d_model 512

S4 gets "best" 87.05% accuracy after 100 epochs @ 3m8s/epoch on a TitanRTX DSS gets "best" 88.90% accuracy after 100 epochs @ 3m11s/epoch on a TitanRTX

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Quickstart (Development)

We have two requirements.txt files that hold dependencies for the current project: one that is tailored to CPUs, the other that installs for GPU.

CPU-Only (MacOS, Linux)

# Set up virtual/conda environment of your choosing & activate...
pip install -r requirements-cpu.txt

# Set up pre-commit
pre-commit install

GPU (CUDA > 11 & CUDNN > 8.2)

# Set up virtual/conda environment of your choosing & activate...
pip install -r requirements-gpu.txt

# Set up pre-commit
pre-commit install

Dependencies from Scratch

In case the above requirements.txt don't work, here are the commands used to download dependencies.

CPU-Only

# Set up virtual/conda environment of your choosing & activate... then install the following:
pip install --upgrade "jax[cpu]"
pip install flax
pip install torch torchvision torchaudio

# Defaults
pip install black celluloid flake8 google-cloud-storage isort ipython matplotlib pre-commit seaborn tensorflow tqdm

# Set up pre-commit
pre-commit install

GPU (CUDA > 11, CUDNN > 8.2)

Note - CUDNN > 8.2 is critical for compilation without warnings, and GPU w/ at least Turing architecture for full efficiency.

# Set up virtual/conda environment of your choosing & activate... then install the following:
pip install jax[cuda11_cudnn82] -f https://storage.googleapis.com/jax-releases/jax_releases.html
pip install flax
pip install torch==1.10.1+cpu torchvision==0.11.2+cpu torchaudio==0.10.1+cpu -f https://download.pytorch.org/whl/cpu/torch_stable.html

# Defaults
pip install black celluloid flake8 google-cloud-storage isort ipython matplotlib pre-commit seaborn tensorflow tqdm

# Set up pre-commit
pre-commit install