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import argparse
import os
import embedder
import torch.distributed as dist
from lmms_eval.api.registry import ALL_TASKS, GROUP_REGISTRY
from lmms_eval.tasks import (
ConfigurableTask,
get_task_dict,
include_path,
initialize_tasks,
)
from lmms_eval.utils import simple_parse_args_string
def rank0_print(*args):
if dist.is_initialized():
if dist.get_rank() == 0:
print(f"Rank {dist.get_rank()}: ", *args)
else:
print(*args)
def parse_arguments():
parser = argparse.ArgumentParser()
parser.add_argument("--name", type=str)
parser.add_argument("--output_path", type=str)
parser.add_argument("--tasks", type=str, required=False, default="")
parser.add_argument("--data_path", type=str, required=False, default="")
parser.add_argument("--image_folder", type=str, required=False, default="")
parser.add_argument("--embedder_kwargs", type=str, default="")
return parser.parse_args()
if __name__ == "__main__":
args = parse_arguments()
embedder_name = args.name
output_path = args.output_path
if args.tasks.lower().strip() == "all":
initialize_tasks()
for task in list(ALL_TASKS):
if task in GROUP_REGISTRY:
ALL_TASKS.remove(task)
tasks = list(ALL_TASKS)
else:
tasks = args.tasks.split(",")
cached_idx = []
for idx in range(len(tasks)):
if os.path.exists(os.path.join(output_path, f"{tasks[idx]}_embed.npy")):
rank0_print(f"Task {tasks[idx]} exists in cache folder, load from cache")
cached_idx.append(idx)
tasks = [tasks[idx] for idx in range(len(tasks)) if idx not in cached_idx]
rank0_print(f"Tasks : {tasks}")
embedder_kwargs = simple_parse_args_string(args.embedder_kwargs)
embedder_cls = getattr(embedder, embedder_name)
embedder_obj = embedder_cls(name=embedder_name, output_path=output_path, **embedder_kwargs)
for task in tasks:
embedder_obj.embed_task(task)
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