[Core] Add multi-step support to LLMEngine (#7789)

This commit is contained in:
Alexander Matveev
2024-08-23 15:45:53 -04:00
committed by GitHub
parent 09c7792610
commit 9db93de20c
7 changed files with 195 additions and 87 deletions

View File

@@ -82,6 +82,8 @@ def run_vllm(
max_num_batched_tokens: int,
distributed_executor_backend: Optional[str],
gpu_memory_utilization: float = 0.9,
num_scheduler_steps: int = 1,
use_v2_block_manager: bool = False,
download_dir: Optional[str] = None,
load_format: str = EngineArgs.load_format,
) -> float:
@@ -106,6 +108,8 @@ def run_vllm(
max_num_batched_tokens=max_num_batched_tokens,
distributed_executor_backend=distributed_executor_backend,
load_format=load_format,
num_scheduler_steps=num_scheduler_steps,
use_v2_block_manager=use_v2_block_manager,
)
# Add the requests to the engine.
@@ -232,7 +236,8 @@ def main(args: argparse.Namespace):
args.quantization_param_path, args.device,
args.enable_prefix_caching, args.enable_chunked_prefill,
args.max_num_batched_tokens, args.distributed_executor_backend,
args.gpu_memory_utilization, args.download_dir, args.load_format)
args.gpu_memory_utilization, args.num_scheduler_steps,
args.use_v2_block_manager, args.download_dir, args.load_format)
elif args.backend == "hf":
assert args.tensor_parallel_size == 1
elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
@@ -353,10 +358,18 @@ if __name__ == "__main__":
choices=["auto", "cuda", "cpu", "openvino", "tpu", "xpu"],
help='device type for vLLM execution, supporting CUDA, OpenVINO and '
'CPU.')
parser.add_argument(
"--num-scheduler-steps",
type=int,
default=1,
help="Maximum number of forward steps per scheduler call.")
parser.add_argument("--use-v2-block-manager",
action='store_true',
help="Enable block manager v2.")
parser.add_argument(
"--enable-prefix-caching",
action='store_true',
help="enable automatic prefix caching for vLLM backend.")
help="Enable automatic prefix caching for vLLM backend.")
parser.add_argument("--enable-chunked-prefill",
action='store_true',
help="enable chunked prefill for vLLM backend.")