trellixvulnteam / flend_p1yu

improved flend

ImprovedFlend

To install and use the training and inference scripts please clone the repo and install the requirements:

git clone repo
cd reo
pip install -r requirements.txt
python -m spacy download en

Installation with Docker

To install using docker please build the self-contained image:

docker build -t convai .

You can then enter the image

ip-192-168-22-157:repo gladaikins$ docker run --rm -it convai bash
root@91e241bb823e:/# ls
Dockerfile  README.md  boot                  dev  home         lib    media  models  proc              root  sbin  sys  train.py  utils.py
LICENCE     bin        convai_evaluation.py  etc  interact.py  lib64  mnt    opt     requirements.txt  run   srv   tmp  usr       var

You can then run the interact.py script on the pretrained model:

python3 interact.py --model models/

The easiest way to download and use this model is just to run the interact.py script to talk with the model. Without any argument, this script will automatically download and cache our model.

Using the training script

The training script can be used in single GPU or multi GPU settings:

python ./train.py  # Single GPU training
python -m torch.distributed.launch --nproc_per_node=8 ./train.py  # Training on 8 GPUs

The training script accept several arguments to tweak the training:

Argument	Type	Default value	Description
dataset_path	str	""	Path or url of the dataset. If empty download from S3.
dataset_cache	str	'./dataset_cache.bin'	Path or url of the dataset cache
model	str	"openai-gpt"	Path, url or short name of the model
num_candidates	int	2	Number of candidates for training
max_history	int	2	Number of previous exchanges to keep in history
train_batch_size	int	4	Batch size for training
valid_batch_size	int	4	Batch size for validation
gradient_accumulation_steps	int	8	Accumulate gradients on several steps
lr	float	6.25e-5	Learning rate
lm_coef	float	1.0	LM loss coefficient
mc_coef	float	1.0	Multiple-choice loss coefficient
max_norm	float	1.0	Clipping gradient norm
n_epochs	int	3	Number of training epochs
personality_permutations	int	1	Number of permutations of personality sentences
device	str	"cuda" if torch.cuda.is_available() else "cpu"	Device (cuda or cpu)
fp16	str	""	Set to O0, O1, O2 or O3 for fp16 training (see apex documentation)
local_rank	int	-1	Local rank for distributed training (-1: not distributed)

Here is how to reproduce our results on a server with 8 V100 GPUs (adapt number of nodes and batch sizes to your configuration):

python -m torch.distributed.launch --nproc_per_node=8 ./train.py --gradient_accumulation_steps=4 --lm_coef=2.0 --max_history=2 --n_epochs=1 --num_candidates=4 --personality_permutations=2 --train_batch_size=2 --valid_batch_size=2

Using the interaction script

The training script saves all the experiments and checkpoints in a sub-folder named with the timestamp of the experiment in the ./runs folder of the repository base folder.

You can then use the interactive script to interact with the model simply by pointing to this folder.

Here is an example command line to run the interactive script:

python ./interact.py --model_checkpoint ./data/Apr17_13-31-38_thunder/  # run the interactive script with a training checkpoint
python ./interact.py  # run the interactive script with the finetuned model on our S3

The fine-tuned model will gives FINAL Hits@1: 0.715

The interactive script accept a few arguments to tweak the decoding algorithm:

Argument	Type	Default value	Description
dataset_path	str	""	Path or url of the dataset. If empty download from S3.
dataset_cache	str	'./dataset_cache.bin'	Path or url of the dataset cache
model	str	"openai-gpt"	Path, url or short name of the model
max_history	int	2	Number of previous utterances to keep in history
device	str	cuda if torch.cuda.is_available() else cpu	Device (cuda or cpu)
no_sample	action store_true	Set to use greedy decoding instead of sampling
max_length	int	20	Maximum length of the output utterances
min_length	int	1	Minimum length of the output utterances
seed	int	42	Seed
temperature	int	0.7	Sampling softmax temperature
top_k	int	0	Filter top-k tokens before sampling (<=0: no filtering)
top_p	float	0.9	Nucleus filtering (top-p) before sampling (<=0.0: no filtering)