""" Benchmark the efficiency of prefix caching. This script allows you to benchmark the performance of a model with and without prefix caching using either fixed prompts or prompts sampled from the ShareGPT dataset. Fixed example usage: python benchmark_prefix_caching.py \ --model meta-llama/Llama-2-7b-chat-hf \ --enable-prefix-caching \ --num-prompts 1 \ --repeat-count 100 ShareGPT example usage: # This command samples 20 prompts with input lengths # between 128 and 256 tokens from the ShareGPT dataset, # then replicates each prompt 5 times. python benchmark_prefix_caching.py \ --model meta-llama/Llama-2-7b-chat-hf \ --dataset-path /path/to/ShareGPT_V3_unfiltered_cleaned_split.json \ --enable-prefix-caching \ --num-prompts 20 \ --repeat-count 5 \ --input-length-range 128:256 """ import json import random import time from typing import List, Optional, Tuple from transformers import PreTrainedTokenizerBase from vllm import LLM, SamplingParams from vllm.utils import FlexibleArgumentParser try: from vllm.transformers_utils.tokenizer import get_tokenizer except ImportError: from backend_request_func import get_tokenizer PROMPT = "You are a helpful assistant in recognizes the content of tables in markdown format. Here is a table as fellows. You need to answer my question about the table.\n# Table\n|Opening|Opening|Sl. No.|Film|Cast|Director|Music Director|Notes|\n|----|----|----|----|----|----|----|----|\n|J A N|9|1|Agni Pushpam|Jayabharathi, Kamalahasan|Jeassy|M. K. Arjunan||\n|J A N|16|2|Priyamvada|Mohan Sharma, Lakshmi, KPAC Lalitha|K. S. Sethumadhavan|V. Dakshinamoorthy||\n|J A N|23|3|Yakshagaanam|Madhu, Sheela|Sheela|M. S. Viswanathan||\n|J A N|30|4|Paalkkadal|Sheela, Sharada|T. K. Prasad|A. T. Ummer||\n|F E B|5|5|Amma|Madhu, Srividya|M. Krishnan Nair|M. K. Arjunan||\n|F E B|13|6|Appooppan|Thikkurissi Sukumaran Nair, Kamal Haasan|P. Bhaskaran|M. S. Baburaj||\n|F E B|20|7|Srishti|Chowalloor Krishnankutty, Ravi Alummoodu|K. T. Muhammad|M. S. Baburaj||\n|F E B|20|8|Vanadevatha|Prem Nazir, Madhubala|Yusufali Kechery|G. Devarajan||\n|F E B|27|9|Samasya|Madhu, Kamalahaasan|K. Thankappan|Shyam||\n|F E B|27|10|Yudhabhoomi|K. P. Ummer, Vidhubala|Crossbelt Mani|R. K. Shekhar||\n|M A R|5|11|Seemantha Puthran|Prem Nazir, Jayabharathi|A. B. Raj|M. K. Arjunan||\n|M A R|12|12|Swapnadanam|Rani Chandra, Dr. Mohandas|K. G. George|Bhaskar Chandavarkar||\n|M A R|19|13|Thulavarsham|Prem Nazir, sreedevi, Sudheer|N. Sankaran Nair|V. Dakshinamoorthy||\n|M A R|20|14|Aruthu|Kaviyoor Ponnamma, Kamalahasan|Ravi|G. Devarajan||\n|M A R|26|15|Swimming Pool|Kamal Haasan, M. G. Soman|J. Sasikumar|M. K. Arjunan||\n\n# Question\nWhat' s the content in the (1,1) cells\n" # noqa: E501 def test_prefix(llm=None, sampling_params=None, prompts=None): start_time = time.time() llm.generate(prompts, sampling_params=sampling_params) end_time = time.time() print(f"cost time {end_time - start_time}") def sample_requests( dataset_path: str, num_requests: int, tokenizer: PreTrainedTokenizerBase, input_length_range: Tuple[int, int], fixed_output_len: Optional[int], ) -> List[Tuple[str, int, int]]: if fixed_output_len is not None and fixed_output_len < 4: raise ValueError("output_len too small") # Load the dataset. with open(dataset_path) as f: dataset = json.load(f) # Filter out the conversations with less than 2 turns. dataset = [data for data in dataset if len(data["conversations"]) >= 2] # Only keep the first two turns of each conversation. dataset = [(data["conversations"][0]["value"], data["conversations"][1]["value"]) for data in dataset] # Shuffle the dataset. random.shuffle(dataset) min_len, max_len = input_length_range # Filter out sequences that are too long or too short filtered_dataset: List[Tuple[str, int, int]] = [] for i in range(len(dataset)): if len(filtered_dataset) == num_requests: break # Tokenize the prompts and completions. prompt = dataset[i][0] prompt_token_ids = tokenizer(prompt).input_ids completion = dataset[i][1] completion_token_ids = tokenizer(completion).input_ids prompt_len = len(prompt_token_ids) output_len = len(completion_token_ids ) if fixed_output_len is None else fixed_output_len if prompt_len < 4 or output_len < 4: # Prune too short sequences. continue if min_len <= prompt_len <= max_len: filtered_dataset.append((prompt, prompt_len, output_len)) return filtered_dataset def repeat_and_sort_requests(requests: List[Tuple[str, int, int]], repeat_count: int, sort: bool = False) -> List[str]: repeated_requests = requests * repeat_count if sort: repeated_requests.sort(key=lambda x: x[1]) else: random.shuffle(repeated_requests) return [req[0] for req in repeated_requests] def main(args): tokenizer = get_tokenizer(args.model, trust_remote_code=True) input_length_range = tuple(map(int, args.input_length_range.split(':'))) if args.dataset_path is not None: print(f"Start to sample {args.num_prompts} prompts" "from {args.dataset_path}") filtered_datasets = sample_requests( dataset_path=args.dataset_path, num_requests=args.num_prompts, tokenizer=tokenizer, input_length_range=input_length_range, fixed_output_len=args.output_len, ) else: prompt_len = len(tokenizer(PROMPT).input_ids) filtered_datasets = [(PROMPT, prompt_len, args.output_len) ] * args.num_prompts llm = LLM(model=args.model, tokenizer_mode='auto', trust_remote_code=True, enforce_eager=True, use_v2_block_manager=args.use_v2_block_manager, tensor_parallel_size=args.tensor_parallel_size, enable_prefix_caching=args.enable_prefix_caching) sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len) print("Testing filtered datasets") prompts = repeat_and_sort_requests(filtered_datasets, repeat_count=args.repeat_count, sort=args.sort) print("------warm up------") test_prefix( llm=llm, prompts=prompts, sampling_params=sampling_params, ) print("------start generating------") test_prefix( llm=llm, prompts=prompts, sampling_params=sampling_params, ) if __name__ == "__main__": parser = FlexibleArgumentParser( description= 'Benchmark the performance with or without automatic prefix caching.') parser.add_argument('--model', type=str, default='baichuan-inc/Baichuan2-13B-Chat') parser.add_argument("--dataset-path", type=str, default=None, help="Path to the dataset.") 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') parser.add_argument('--use-v2-block-manager', action='store_true', help='Use BlockSpaceMangerV2') parser.add_argument('--num-prompts', type=int, default=1, help="Number of the prompts sampled from dataset") parser.add_argument('--repeat-count', type=int, default=100, help='Number of times to repeat each prompt') parser.add_argument('--sort', action='store_true', help='Sort prompts by input length') parser.add_argument('--input-length-range', type=str, default='128:256', help='Range of input lengths for sampling prompts,' 'specified as "min:max" (e.g., "128:256").') args = parser.parse_args() main(args)