def main(args=None, model=None) -> GenerativeQAModule:
Path(args.output_dir).mkdir(exist_ok=True)
# named_actors = []
# args.actor_handles = named_actors
# assert args.actor_handles == named_actors
if model is None:
model: GenerativeQAModule = GenerativeQAModule(args)
dataset = Path(args.data_dir).name
data_module = Seq2SeqDataModule(model.tokenizer, args)
if (args.logger_name == "default" or args.fast_dev_run
or str(args.output_dir).startswith("/tmp")
or str(args.output_dir).startswith("/var")):
training_logger = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
project = os.environ.get("WANDB_PROJECT", dataset)
training_logger = WandbLogger(name=model.output_dir.name,
project=project)
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
training_logger = WandbLogger(name=model.output_dir.name,
project=f"hf_{dataset}")
elif args.logger_name == "tb-logs":
from pytorch_lightning.loggers import TensorBoardLogger
training_logger = TensorBoardLogger('tb_logs', name='my_model')
es_callback = (get_early_stopping_callback(model.val_metric,
args.early_stopping_patience)
if args.early_stopping_patience >= 0 else False)
trainer: pl.Trainer = generic_train(
model,
args,
data_module,
logging_callback=Seq2SeqLoggingCallback(),
checkpoint_callback=get_checkpoint_callback(args.output_dir,
model.val_metric),
early_stopping_callback=es_callback,
logger=training_logger,
accelerator=CustomAccel() if args.gpus > 1 else None,
profiler=pl.profiler.AdvancedProfiler() if args.profile else None,
)
pickle_save(model.hparams, model.output_dir / "hparams.pkl")
if not args.do_predict:
return model
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
def main(args=None, model=None) -> GenerativeQAModule:
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd())
parser = GenerativeQAModule.add_retriever_specific_args(parser)
args = args or parser.parse_args()
Path(args.output_dir).mkdir(exist_ok=True)
if model is None:
model: GenerativeQAModule = GenerativeQAModule(args)
dataset = Path(args.data_dir).name
if (args.logger_name == "default" or args.fast_dev_run
or str(args.output_dir).startswith("/tmp")
or str(args.output_dir).startswith("/var")):
logger = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
project = os.environ.get("WANDB_PROJECT", dataset)
logger = WandbLogger(name=model.output_dir.name, project=project)
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
logger = WandbLogger(name=model.output_dir.name,
project=f"hf_{dataset}")
es_callback = (get_early_stopping_callback(model.val_metric,
args.early_stopping_patience)
if args.early_stopping_patience >= 0 else False)
trainer: pl.Trainer = generic_train(
model,
args,
logging_callback=Seq2SeqLoggingCallback(),
checkpoint_callback=get_checkpoint_callback(args.output_dir,
model.val_metric),
early_stopping_callback=es_callback,
logger=logger,
accelerator=CustomAccel() if args.gpus > 1 else None,
)
pickle_save(model.hparams, model.output_dir / "hparams.pkl")
if not args.do_predict:
return model
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
def main(args=None, model=None) -> GenerativeQAModule:
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
parser = GenerativeQAModule.add_model_specific_args(parser, os.getcwd())
parser = GenerativeQAModule.add_retriever_specific_args(parser)
args = args or parser.parse_args()
Path(args.output_dir).mkdir(exist_ok=True)
Path(args.output_dir + "/dpr_ctx_checkpoint").mkdir(
exist_ok=True) # save dpr_context encoder seprately for the future use
print(args.shard_dir)
if os.path.exists(
args.shard_dir
): # we do not need previous kb shards used in dataset re-conding and re-indexing
shutil.rmtree(args.shard_dir)
Path(args.shard_dir).mkdir(exist_ok=True)
if os.path.exists(
args.cache_dir
): # we do not need previous cache files used in dataset re-conding and re-indexing
shutil.rmtree(args.cache_dir)
Path(args.cache_dir).mkdir(exist_ok=True)
named_actors = []
if args.distributed_retriever == "ray" and args.gpus > 1:
if not is_ray_available():
raise RuntimeError("Please install Ray to use the Ray "
"distributed retriever.")
# Connect to an existing Ray cluster.
try:
ray.init(address=args.ray_address)
except (ConnectionError, ValueError):
logger.warning(
"Connection to Ray cluster failed. Make sure a Ray"
"cluster is running by either using Ray's cluster "
"launcher (`ray up`) or by manually starting Ray on "
"each node via `ray start --head` for the head node "
"and `ray start --address='<ip address>:6379'` for "
"additional nodes. See "
"https://docs.ray.io/en/master/cluster/index.html "
"for more info.")
raise
# Create Ray actors only for rank 0.
if ("LOCAL_RANK" not in os.environ or os.environ["LOCAL_RANK"]
== 0) and ("NODE_RANK" not in os.environ
or os.environ["NODE_RANK"] == 0):
remote_cls = ray.remote(RayRetriever)
named_actors = [
remote_cls.options(
name="retrieval_worker_{}".format(i)).remote()
for i in range(args.num_retrieval_workers)
]
else:
logger.info(
"Getting named actors for NODE_RANK {}, LOCAL_RANK {}".format(
os.environ["NODE_RANK"], os.environ["LOCAL_RANK"]))
named_actors = [
ray.get_actor("retrieval_worker_{}".format(i))
for i in range(args.num_retrieval_workers)
]
args.actor_handles = named_actors
assert args.actor_handles == named_actors
if model is None:
model: GenerativeQAModule = GenerativeQAModule(args)
dataset = Path(args.data_dir).name
if (args.logger_name == "default" or args.fast_dev_run
or str(args.output_dir).startswith("/tmp")
or str(args.output_dir).startswith("/var")):
training_logger = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
project = os.environ.get("WANDB_PROJECT", dataset)
training_logger = WandbLogger(name=model.output_dir.name,
project=project)
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
training_logger = WandbLogger(name=model.output_dir.name,
project=f"hf_{dataset}")
es_callback = (get_early_stopping_callback(model.val_metric,
args.early_stopping_patience)
if args.early_stopping_patience >= 0 else False)
trainer: pl.Trainer = generic_train(
model,
args,
logging_callback=Seq2SeqLoggingCallback(),
checkpoint_callback=get_checkpoint_callback(args.output_dir,
model.val_metric),
early_stopping_callback=es_callback,
logger=training_logger,
profiler=pl.profiler.AdvancedProfiler() if args.profile else None,
)
pickle_save(model.hparams, model.output_dir / "hparams.pkl")
if not args.do_predict:
return model
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model