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import os
import torch.cuda
import wandb
import argparse
import pytorch_lightning as pl
from pytorch_lightning.loggers import WandbLogger
from termcolor import colored
from transformers import BertForMaskedLM, BertTokenizer
from idiomify.datamodules import IdiomifyDataModule
from idiomify.fetchers import fetch_config, fetch_idioms
from idiomify.models import Alpha, Gamma
from idiomify.paths import ROOT_DIR
from idiomify import tensors as T
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--model", type=str, default="alpha")
parser.add_argument("--ver", type=str, default="eng2eng")
parser.add_argument("--num_workers", type=int, default=os.cpu_count())
parser.add_argument("--log_every_n_steps", type=int, default=1)
parser.add_argument("--fast_dev_run", action="store_true", default=False)
parser.add_argument("--upload", dest='upload', action='store_true', default=False)
args = parser.parse_args()
config = fetch_config()[args.model][args.ver]
config.update(vars(args))
if not config['upload']:
print(colored("WARNING: YOU CHOSE NOT TO UPLOAD. NOTHING BUT LOGS WILL BE SAVED TO WANDB", color="red"))
# prepare arguments
mlm = BertForMaskedLM.from_pretrained(config['bert'])
tokenizer = BertTokenizer.from_pretrained(config['bert'])
idioms = fetch_idioms(config['idioms_ver'])
idiom2subwords = T.idiom2subwords(idioms, tokenizer, config['k'])
# choose the model to train
if config['model'] == Alpha.name():
rd = Alpha(mlm, idiom2subwords, config['k'], config['lr'])
elif config['model'] == Gamma.name():
rd = Gamma(mlm, idiom2subwords, config['k'], config['lr'])
else:
raise ValueError
# prepare datamodule
datamodule = IdiomifyDataModule(config, tokenizer, idioms)
with wandb.init(entity="eubinecto", project="idiomify-demo", config=config) as run:
logger = WandbLogger(log_model=False)
trainer = pl.Trainer(max_epochs=config['max_epochs'],
fast_dev_run=config['fast_dev_run'],
log_every_n_steps=config['log_every_n_steps'],
gpus=torch.cuda.device_count(),
default_root_dir=str(ROOT_DIR),
enable_checkpointing=False,
logger=logger)
# start training
trainer.fit(model=rd, datamodule=datamodule)
# upload the model to wandb only if the training is properly done #
if not config['fast_dev_run'] and trainer.current_epoch == config['max_epochs'] - 1:
ckpt_path = ROOT_DIR / "rd.ckpt"
trainer.save_checkpoint(str(ckpt_path))
artifact = wandb.Artifact(name=config['model'], type="model", metadata=config)
artifact.add_file(str(ckpt_path))
run.log_artifact(artifact, aliases=["latest", config['ver']])
os.remove(str(ckpt_path)) # make sure you remove it after you are done with uploading it
if __name__ == '__main__':
main()
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