# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import cProfile import pstats from vllm import LLM, SamplingParams from vllm.utils import FlexibleArgumentParser # A very long prompt, total number of tokens is about 15k. LONG_PROMPT = ["You are an expert in large language models, aren't you?"] * 1000 LONG_PROMPT = " ".join(LONG_PROMPT) def main(args): llm = LLM( model=args.model, enforce_eager=True, enable_prefix_caching=True, tensor_parallel_size=args.tensor_parallel_size, ) sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len) profiler = cProfile.Profile() print("------warm up------") for i in range(3): output = llm.generate(LONG_PROMPT, sampling_params) print(output[0].outputs[0].text) print("------start generating------") for i in range(3): profiler.runctx( "llm.generate(LONG_PROMPT, sampling_params)", globals(), locals() ) # analyze the runtime of hashing function stats = pstats.Stats(profiler) stats.sort_stats("cumulative") total_time = 0 total_calls = 0 for func in stats.stats: if "hash_of_block" in func[2]: total_time = stats.stats[func][3] total_calls = stats.stats[func][0] percentage = (total_time / stats.total_tt) * 100 print( f"Hashing took {total_time:.2f} seconds,{percentage:.2f}% of the total runtime." ) if __name__ == "__main__": parser = FlexibleArgumentParser( description="Benchmark the performance of hashing function in" "automatic prefix caching." ) parser.add_argument("--model", type=str, default="lmsys/longchat-7b-16k") parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1) parser.add_argument("--output-len", type=int, default=10) parser.add_argument( "--enable-prefix-caching", action="store_true", help="enable prefix caching" ) args = parser.parse_args() main(args)