save_sharded_state.py

# 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/to/load \
    --quantization deepspeedfp \
    --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",
    quantization="deepspeedfp",
    tensor_parallel_size=8,
)
"""
import dataclasses
import os
import shutil
from pathlib import Path

from vllm import LLM, EngineArgs
from vllm.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,
                        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")
    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

    # Check which engine version is being used
    is_v1_engine = hasattr(llm.llm_engine, "engine_core")

    if is_v1_engine:
        # For V1 engine, we need to use engine_core.save_sharded_state
        print("Using V1 engine save path")
        llm.llm_engine.engine_core.save_sharded_state(
            path=args.output,
            pattern=args.file_pattern,
            max_size=args.max_file_size)
    else:
        # For V0 engine
        print("Using V0 engine save path")
        model_executor = llm.llm_engine.model_executor
        model_executor.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)