def cli_main():
try:
from hydra._internal.utils import get_args
cfg_name = get_args().config_name or "config"
except:
logger.warning("Failed to get config name from hydra args")
cfg_name = "config"
hydra_init(cfg_name)
hydra_main()
__all__ = ["pdb"] # backwards compatibility to support `from fairseq.X import Y` from fairseq.distributed import utils as distributed_utils from fairseq.logging import meters, metrics, progress_bar # noqa sys.modules["fairseq.distributed_utils"] = distributed_utils sys.modules["fairseq.meters"] = meters sys.modules["fairseq.metrics"] = metrics sys.modules["fairseq.progress_bar"] = progress_bar # initialize hydra from fairseq.dataclass.initialize import hydra_init hydra_init() import fairseq.criterions # noqa import fairseq.distributed # noqa import fairseq.models # noqa import fairseq.modules # noqa import fairseq.optim # noqa import fairseq.optim.lr_scheduler # noqa import fairseq.pdb # noqa import fairseq.scoring # noqa import fairseq.tasks # noqa import fairseq.token_generation_constraints # noqa import fairseq.benchmark # noqa import fairseq.model_parallel # noqa
@hydra.main(config_path=os.path.join("..", "fairseq", "config"),
config_name="config")
def hydra_main(cfg: FairseqConfig) -> None:
cfg = OmegaConf.create(
OmegaConf.to_container(cfg, resolve=True, enum_to_str=True))
OmegaConf.set_struct(cfg, True)
if cfg.common.profile:
with torch.cuda.profiler.profile():
with torch.autograd.profiler.emit_nvtx():
distributed_utils.call_main(cfg, pre_main)
else:
distributed_utils.call_main(cfg, pre_main)
if __name__ == "__main__":
try:
from hydra._internal.utils import get_args
cfg_name = get_args().config_name or "config"
except:
logger.warning("Failed to get config name from hydra args")
cfg_name = "config"
hydra_init(cfg_name)
hydra_main()