Polygames

This README is a work in progress, please feel very free to post issues - we are happy to help. Save up computational power: you can find checkpoints here: http://dl.fbaipublicfiles.com/polygames/checkpoints/list.txt (feel free to open an issue for discussing which checkpoint you should use for which game/problem!).

For Nix users: see this doc.

Requirement:

C++14 compatible compiler
miniconda3

Compilation Guide using modules:

First install conda and pytorch

Create a fresh conda environment with python3.7 and compile pytorch from scratch.

Instruction for building on devfair:

# loading proper modules on devfair
module purge
module load anaconda3
module load cudnn/v7.4-cuda.10.0
module load cuda/10.0

# create a fresh conda environment with python3
# you will need to have miniconda3 set up
conda create --name [your env name] python=3.7 pip

conda activate [your env name] # Or source activate [your env name], depending on conda version.

conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing
conda install -c pytorch magma-cuda100
conda install -c conda-forge tensorboardx
pip install visdom

# clone the repo
# Note: put the repo onto /scratch partition for MUCH FASTER building speed.
git clone --recursive https://github.com/pytorch/pytorch --branch=v1.1.0
cd pytorch

export CMAKE_PREFIX_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
# set cuda arch list so that the built binary can be run on both pascal and volta
TORCH_CUDA_ARCH_LIST="6.0;7.0" python setup.py install

Note that the CMake needs to find the same Python for which pytorch is compiled against during the subsequent compilation process to avoid any compatibility issue. We can modidy torchRL/tube/CMakeLists.txt to select the correct PythonLib if necessary.

Clone the repo and build

git clone ...
mkdir build
cd build
cmake ..
make

On devfair, you may need to sudo ln -s /public/apps/cuda/10.0 /usr/local/cuda if you see the error No rule to make target '/usr/local/cuda/lib64/libculibos.a'

Troubleshooting

Undefined references to several BLAS methods, e.g.

/private/home/xxx/.conda/envs/polygames/lib/python3.7/site-packages/torch/lib/libcaffe2.so: undefined reference to `cblas_sgemm_pack_get_size'
/private/home/xxx/.conda/envs/polygames/lib/python3.7/site-packages/torch/lib/libcaffe2.so: undefined reference to `cblas_gemm_s8u8s32_compute'
/private/home/xxx/.conda/envs/polygames/lib/python3.7/site-packages/torch/lib/libcaffe2.so: undefined reference to `cblas_gemm_s8u8s32_pack_get_size'

Solution 1: prioritize mkl from your environment (e.g. over the one in /public/apps/anaconda3/5.0.1/lib). For that

check that mkl from the environment contains the required methods:

nm ${CONDA_PREFIX:-"$(dirname $(which conda))/../"}/lib/libmkl_rt.so | grep cblas_gemm_s8u8s32_compute

or more generally

METHOD_NAME=cblas_gemm_s8u8s32_compute; \
ls -1 ${CONDA_PREFIX:-"$(dirname $(which conda))/../"}/lib/libmkl_*.so | \
xargs -i sh -c "echo Looking for $METHOD_NAME in {} ; nm {};" | \
grep $METHOD_NAME

Expected output example:

Looking for cblas_gemm_s8u8s32_compute in <path_to_env>/lib/libmkl_rt.so
(...) T cblas_gemm_s8u8s32_compute

update your LD_LIBRARY_PATH:

export LD_LIBRARY_PATH=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}/lib/:$LD_LIBRARY_PATH

Solution 2: add stub implementations. Add to a cpp source file (e.g., torchRL/tube/src_cpp/data_channel.cc) the following snippet:

extern "C" void cblas_gemm_s8u8s32_pack() {
    std::terminate();
}
extern "C" void cblas_sgemm_pack_get_size() {
    std::terminate();
}
extern "C" void cblas_gemm_s8u8s32_compute() {
    std::terminate();
}
extern "C" void cblas_gemm_s8u8s32_pack_get_size() {
    std::terminate();
}

Compilation Guide without modules:

First install conda and pytorch

create a fresh conda environment with python3 you will need to have miniconda3 set up

conda create --name [your env name] python=3.7 pip

conda activate [your env name] # Or source activate [your env name], depending on conda version.

conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing
conda install -c pytorch magma-cuda100
conda install -c conda-forge tensorboardx
pip install visdom
conda install pytorch=1.1.0 cuda92 -c pytorch [this version is compatible with cuda9]

Clone the repo and build

git clone ... 
cd polygames
mkdir build
cd build
cmake .. [gcc>=7]
make

Content

The repo contains mostly the following folders:

the pypolygames python package, which serves as an entry point for the application
the torchRL folder, containing C++ bindings for python
the games folder, containing the games coded in C++

How to use the application

The application is launched from the pypolygames python package, in either of the following modes:ar

pypolygames train (training mode): a game and a model (as well as several other options, see below) are chosen and the model is iteratively trained with MCTS
pypolygames eval (evaluation mode): the model confronts either a pure MCTS or another neural network powered MCTS. The evaluation of a training can be done either offline (from checkpoints periodically saved) or in real time; in that case, the evaluation considers only the most recent checkpoint in order to follow closely the training, skipping some checkpoints in case the eval computation takes longer than the time becween consecutive checkpoints. It is displayed through visdom.
pypolygames traineval (training + evaluation mode): it mixes the two previous modes and allow to launch one command instead of two. With the real_time option the modes can be launched in parallel instead of sequentially.
pypolygames human (human mode): a human player plays against the machine

When a training is launched, it creates a game_GAMENAME_model_MODELNAME_feat_FEATURIZATION_GMT_YYYYMMDDHHMMSS within the save_dir where it will log relevant files:

model.pt
train.log
stat.tb
checkpoints_EPOCH.pt for for checkpoints saved each saving_period epoch (e.g., if saving_period == 10, checkpoints_0.pt, checkpoints_10.pt, checkpoints_20.pt, checkpoints_30.pt)

This directory will be the checkpoint_save_dir directory used by evaluation to retrieve the checkpoints to perform eval computation.

Parameters

The list of parameters for each mode is available with

python -m pypolygames {train,eval,traineval,human} --help

Threads

In train (resp. eval) mode, num_game * num_actor (resp. num_game * num_actor_eval * num_actor_opponent) is the total number of threads. The more num_actor (and num_actor_eval, num_actor_opponent), the larger the MCTS is for a given player.

In human mode, since num_game is set to one, for leveraging the computing power available on the platform, a rule-of-thumb is to set num_actor to 5 times the number of CPUs available (it is platform-dependent though, and performance tests should be done).

Model zoo

All models can be found in pypolygames/model_zoo. They come with a set of sensible parameters that can be customized as well as default games.

Usually models come in pair: MODELNAMEFCLogitModel and MODELNAMEConvLogitModel:

FCLogit models use a fully-connected layer for logit inference and are compatible with all games
ConvLogit models use a convolutional layer for logit inference and are only compatible with games whose action space if of same dimensions than their input space (an exception will be raised in case of an attempt to use an incompatible game)

So far the models being implemented are the folling:

GenericModel: generic model compatible with all games, default when no model_name is specified
NanoFCLogitModel: a simple model with a logit-inference fully-connected layer
NanoConvLogitModel: a simple model with a logit-inference convolutional layer
ResConvFCLogitModel: resnets with a logit-inference fully-connected layer
ResConvConvLogitModel: resnets with a logit-inference convolutional layer
UConvFCLogitModel: unets (direct paths between first and last layers) with a logit-inference fully-connected layer
UConvConvLogitModel: unets (direct paths between first and last layers) with a logit-inference convolutional layer
AmazonsModel: only for the Amazons game

Depending on the actual model chosen, some parameters might not have any use.

Featurization

--out_features=True: the input to the NN includes a channel with 1 on the frontier.
--turn_features=True: the input to the NN includes a channel with the player index broadcasted.
--geometric_features=True: the input to the NN includes 4 geometric channels representing the position on the board.
--random_features=4: the input to the NN includes 4 random features.
--one_feature=True: the input to the NN includes a channel with 1 everywhere.
--history=3: the representation from the last 3 steps is added in the featurization.

Examples

Run the following command before running the code

export OMP_NUM_THREADS=1

Examples for the training mode

Launch the game Connect4 with the GenericModel

python -m pypolygames train --game_name="Connect4"

Launch a game with a specific model and specific parameters

python -m pypolygames train --game_name="Connect4" --out_features=True \
    --model_name="UConvFCLogitModel" \
    --nnsize=16 \
    --nnks=3 \
    --pooling

Save checkpoints every 20 epochs in a specific folder

python -m pypolygames train --game_name="Connect4" --model_name="UConvFCLogitModel" \
    --saving_period=20 \
    --save_dir="/checkpoints"

Run training on GPU for a max time

python -m pypolygames train --game_name="Connect4" --model_name="UConvFCLogitModel" \
    --device="cuda:0" \
    --max_time=3600

Resume training from a given epoch

python -m pypolygames train \
    --save_dir="/checkpoints/game_Connect4_model_GenericModel_feat..._GMT_20190717103728" \
    --init_epoch=42

Initiate from a pretrained model

python -m pypolygames train --init_checkpoint="path/to/pretrained_model.pt" \
    --lr=0.001

Note that any checkpoint can serve as a pretrained model

Train on multiple GPUs

python -m pypolygames train --init_checkpoint "path/to/pretrained_model.pt" \
    --device cuda:0 cuda:1 cuda:2 cuda:3 cuda:4

In this case cuda:0 will be used for training the model while cuda:1, cuda:2 and cuda:3 will be used for generating games. If there is only one device specified, it will be used for both purposes.

Notes:

the total number of threads is given by num_game * num_actor
in training mode it is better to leave num_actor to its default value of 1
num_game should be set to the number of available cores on the training platform
within each threads, per_thread_batchsize games will be played sequentially and batched together; a sensible value to take advantage of GPUs is to set it to 256
there is a tradeoff between CPU utilisation and GPU utilisation; since CPU utilisation is much more sentitive to per_thread_batchsize than GPU utilisation, it is probably safer to set this parameter on the conservative side (e.g., 256)
the performance of a training is highly dependent on the MCTS size, the larger the better - to the extent of time taken to generate rollouts is not detrimental to the training time (it seems that 1600 is a sensible default)
small models and small num_rollouts can lead to a Red Queen Effect: competing against itself, the model learns to adapt to itself then beats itself, which incur cycles in win rate vs num_epoch. The solution is to increase the model size and/or num_rollouts
checkpoints are by default compressed with gzip and store the replay buffer in order to facilitate resuming training or fine-tuning

Examples for the evaluation mode

Run offline evaluation

python -m pypolygames eval \
    --checkpoint_dir="/checkpoints/game_Connect4_model_GenericModel_feat..._GMT_20190717103728"

Plot evaluation on http://localhost:10000 as the same time as training happens (training needs to be run from another process)

python -m pypolygames eval \
    --checkpoint_dir="/checkpoints/game_Connect4_model_GenericModel_feat..._GMT_20190717103728" \
    --real_time \
    --plot_enabled \
    --plot_port=10000

Run evaluation on cpu with 100 games per evaluation, the pure-MCTS opponent playing 1000 rollouts while the model plays 400 rollouts

python -m pypolygames eval \
    --checkpoint_dir="/checkpoints/game_Connect4_model_GenericModel_feat..._GMT_20190717103728" \
    --device_eval="cpu" \
    --num_game_eval=100 \
    --num_rollouts_eval=400 \
    --num_actor_eval=8 \
    --num_rollouts_opponent=1000 \
    --num_actor_opponent=8

A specific checkpoint plays against another neural-network-powered MCTS

python -m pypolygames eval \
    --checkpoint="/checkpoints/checkpoint_600.zip" \
    --num_rollouts_eval=400 \
    --num_actor_eval=8 \
    --checkpoint_opponent="/checkpoints/checkpoint_200.zip" \
    --num_rollouts_opponent=1000 \
    --num_actor_opponent=8

Four GPUs are used for evaluating the model, all for inference

python -m pypolygames eval \
    --checkpoint="/checkpoints/checkpoint_600.zip" \
    --device_eval cuda:0 cuda:1 cuda:2 cuda:3 \
    --num_rollouts_eval=400 \
    --num_actor_eval=8 \
    --num_rollouts_opponent=1000 \
    --num_actor_opponent=8

Notes:

num_actor_eval, num_rollouts_eval, num_actor_opponent and num_rollouts_opponent are independent from the values used during training; in particular for proper benchmarking num_actor_eval and num_rollouts_eval should be set to the values used in human mode
as for training, num_game_eval * num_actor_eval (resp. num_game_eval * num_actor_opponent) is the number of threads used by the model to be evaluated (resp. the opponent)
there is no per_thread_batchsize in this mode
the higher num_actor_eval (resp. num_actor_opponent), the larger MCTS for a move in a given game will be, up to a limit where overheads between threads lead to decreasing returns. Empiracally this limit seems to be around 8. This limit may be game/model/platform dependent and should be tuned for a given instance.
against a pure MCTS opponent, num_rollouts_opponent should be set significantly higher than num_rollouts_eval

Examples for the training+evaluation mode

Run first training then evaluation on the last checkpoint

python -m pypolygames traineval --game_name="Connect4" \
    --save_dir="/checkpoints" \
    --num_epoch=1000

Plot evaluation on http://localhost:10000 as the same time as training happens

python -m pypolygames traineval --game_name="Connect4" \
    --save_dir="/checkpoints" \
    --real_time \
    --plot_enabled \
    --plot_port=10000

Examples for the human mode

Play to Connect4 against a pure MCTS as the second player with 8 threads

python -m pypolygames human --game_name="Connect4" \
    --pure_mcts \
    --num_actor 8

Play to Connect4 against a pretrained model as the second player

python -m pypolygames human \
    --init_checkpoint="/checkpoints/checkpoint_600.zip" \
    --human_first

Play with a timer, each side having 1800s in total, and the model playing each move with 0.07 of the remaining time

python -m pypolygames human \
    --init_checkpoint="/checkpoints/checkpoint_600.zip" \
    --total_time=1800 \
    --time_ratio=0.07

The model uses four GPUs, all for inference

python -m pypolygames human \
    --init_checkpoint "/checkpoints/checkpoint_600.zip" \
    --device cuda:0 cuda:1 cuda:2 cuda:3

The model uses four GPUs, all for inference, and uses the text protocol (actions are represented by x y z, each on one line):

python -m pypolygames tp \
    --init_checkpoint "/checkpoints/checkpoint_600.zip" \
    --device cuda:0 cuda:1 cuda:2 cuda:3

Notes:

in human mode, the model being fixed, the goal is to maximize performance given the platform running the model
the most effective way to improve model performance is to increase the MCTS size
as for training and evaluation, but given that there is only one game played, num_actor is the total number of threads
the higher num_actor, the larger the MCTS, up to a limit where overheads between threads lead to decreasing returns. Empiracally this limit seems to be around 8. This limit may be game/model/platform dependent and should be tuned for a given instance.
in a time-limited game num_rollouts should not be specified as it is maximized within each time_ratio * remaining time period

Contributing

We welcome contributions! Please check basic instructutions here

Initial contributors

Contributors to the early version of Polygames (before open source release) include:

Tristan Cazenave, Univ. Dauphine; Yen-Chi Chen, National Taiwan Normal University; Guan-Wei Chen, National Dong Hwa University; Shi-Yu Chen, National Dong Hwa University; Xian-Dong Chiu, National Dong Hwa University; Julien Dehos, Univ. Littoral Cote d’Opale; Maria Elsa, National Dong Hwa University; Qucheng Gong, Facebook AI Research; Hengyuan Hu, Facebook AI Research; Vasil Khalidov, Facebook AI Research; Chen-Ling Li, National Dong Hwa University; Hsin-I Lin, National Dong Hwa University; Yu-Jin Lin, National Dong Hwa University; Xavier Martinet, Facebook AI Research; Vegard Mella, Facebook AI Research; Jeremy Rapin, Facebook AI Research; Baptiste Roziere, Facebook AI Research; Gabriel Synnaeve, Facebook AI Research; Fabien Teytaud, Univ. Littoral Cote d’Opale; Olivier Teytaud, Facebook AI Research; Shi-Cheng Ye, National Dong Hwa University; Yi-Jun Ye, National Dong Hwa University; Shi-Jim Yen, National Dong Hwa University; Sergey Zagoruyko, Facebook AI Research

License

polygames is released under the MIT license. See LICENSE for additional details about it. Third-party libraries are also included under their own license.

gemasphi/Polygames

Polygames

Requirement:

Compilation Guide using modules:

First install conda and pytorch

Clone the repo and build

Troubleshooting

Compilation Guide without modules:

First install conda and pytorch

Clone the repo and build

Content

How to use the application

Parameters

Threads

Model zoo

Featurization

Examples

Examples for the training mode

Examples for the evaluation mode

Examples for the training+evaluation mode

Examples for the human mode

Contributing

Initial contributors

License