Gahow Wang
445e491123
third_party/vllm/ now tracked in git for direct patch management.
Based on vLLM v0.18.1 release with one patch applied:
vllm/v1/core/sched/scheduler.py:
Replace fatal assert with graceful skip when KV transfer callback
arrives for an already-aborted request during PD disaggregated serving.
Future vLLM modifications should be made directly in third_party/vllm/
and committed normally. The patches/ directory is kept as documentation
of what changed from upstream.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
86 lines
2.5 KiB
Python
86 lines
2.5 KiB
Python
# SPDX-License-Identifier: Apache-2.0
|
|
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
|
|
"""
|
|
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/to/load \
|
|
--tensor-parallel-size 8 \
|
|
--output /path/to/save
|
|
|
|
Then, the model can be loaded with
|
|
|
|
llm = LLM(
|
|
model="/path/to/save",
|
|
load_format="sharded_state",
|
|
tensor_parallel_size=8,
|
|
)
|
|
"""
|
|
|
|
import dataclasses
|
|
import os
|
|
import shutil
|
|
from pathlib import Path
|
|
|
|
from vllm import LLM, EngineArgs
|
|
from vllm.model_executor.model_loader import ShardedStateLoader
|
|
from vllm.utils.argparse_utils import FlexibleArgumentParser
|
|
|
|
|
|
def parse_args():
|
|
parser = FlexibleArgumentParser()
|
|
EngineArgs.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,
|
|
default=ShardedStateLoader.DEFAULT_PATTERN,
|
|
help="string pattern of saved filenames",
|
|
)
|
|
parser.add_argument(
|
|
"--max-file-size",
|
|
type=int,
|
|
default=5 * 1024**3,
|
|
help="max size (in bytes) of each safetensors file",
|
|
)
|
|
return parser.parse_args()
|
|
|
|
|
|
def main(args):
|
|
engine_args = EngineArgs.from_cli_args(args)
|
|
if engine_args.enable_lora:
|
|
raise ValueError("Saving with enable_lora=True is not supported!")
|
|
model_path = engine_args.model
|
|
if not Path(model_path).is_dir():
|
|
raise ValueError("model path must be a local directory")
|
|
# Create LLM instance from arguments
|
|
llm = LLM(**dataclasses.asdict(engine_args))
|
|
# Prepare output directory
|
|
Path(args.output).mkdir(exist_ok=True)
|
|
# Dump worker states to output directory
|
|
|
|
llm.llm_engine.engine_core.save_sharded_state(
|
|
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 = parse_args()
|
|
main(args)
|