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chore: import upstream snapshot with attribution
2026-07-13 12:38:16 +08:00

76 lines
2.1 KiB
Python

# SPDX-License-Identifier: Apache-2.0
"""
Saves each worker's model state dict directly to a checkpoint, which enables a
fast load path for large tensor-parallel models where each worker only needs to
read its own shard rather than the entire checkpoint.
Example usage:
python save_sharded_state.py \
--model-path /path/to/load \
--quantization deepspeedfp \
--tensor-parallel-size 8 \
--output /path/to/save
Then, the model can be loaded with
llm = Engine(
model_path="/path/to/save",
load_format="sharded_state",
quantization="deepspeedfp",
tensor_parallel_size=8,
)
"""
import dataclasses
import os
import shutil
from argparse import ArgumentParser
from pathlib import Path
from sglang import Engine, ServerArgs
parser = ArgumentParser()
ServerArgs.add_cli_args(parser)
parser.add_argument(
"--output", "-o", required=True, type=str, help="path to output checkpoint"
)
parser.add_argument(
"--file-pattern", type=str, help="string pattern of saved filenames"
)
parser.add_argument(
"--max-file-size",
type=str,
default=5 * 1024**3,
help="max size (in bytes) of each safetensors file",
)
def main(args):
engine_args = ServerArgs.from_cli_args(args)
model_path = engine_args.model_path
if not Path(model_path).is_dir():
raise ValueError("model path must be a local directory")
# Create LLM instance from arguments
llm = Engine(**dataclasses.asdict(engine_args))
Path(args.output).mkdir(exist_ok=True)
llm.save_sharded_model(
path=args.output, pattern=args.file_pattern, max_size=args.max_file_size
)
# Copy metadata files to output directory
for file in os.listdir(model_path):
if os.path.splitext(file)[1] not in (".bin", ".pt", ".safetensors"):
if os.path.isdir(os.path.join(model_path, file)):
shutil.copytree(
os.path.join(model_path, file), os.path.join(args.output, file)
)
else:
shutil.copy(os.path.join(model_path, file), args.output)
if __name__ == "__main__":
args = parser.parse_args()
main(args)