jump-forward rename (#144)
This commit is contained in:
Liangsheng Yin
74
benchmark/json_jump_forward/README.md
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74
benchmark/json_jump_forward/README.md
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@@ -0,0 +1,74 @@
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## Run benchmark
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### Dependencies
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```
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llama_cpp_python 0.2.38
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guidance 0.1.10
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vllm 0.2.7
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outlines 0.0.25
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```
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### Build dataset
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When benchmarking long document information retrieval, run the following command to build the dataset:
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```bash
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pip install wikipedia
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python3 build_dataset.py
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```
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### Benchmark sglang
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Run Llama-7B
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```bash
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python3 -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
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```
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Benchmark Character Generation
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```bash
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python3 bench_sglang.py --mode character
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```
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Benchmark City Information Retrieval
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```bash
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python3 bench_sglang.py --mode city
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```
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### Benchmark vllm
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Run Llama-7B
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```bash
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python3 -m outlines.serve.serve --tokenizer-mode auto --model meta-llama/Llama-2-7b-chat-hf --disable-log-requests --port 21000
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```
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Benchmark Character Generation
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```bash
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python3 bench_other.py --mode character --backend vllm
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```
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Benchmark City Information Retrieval
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```bash
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python3 bench_other.py --mode city --backend vllm
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```
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### Benchmark guidance
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Run Llama-7B and benchmark character generation
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```bash
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python3 bench_other.py --mode character --backend guidance --parallel 1
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```
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Run Llama-7B and benchmark city information retrieval
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```bash
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python3 bench_other.py --mode city --backend guidance --parallel 1
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```
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245
benchmark/json_jump_forward/bench_other.py
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245
benchmark/json_jump_forward/bench_other.py
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@@ -0,0 +1,245 @@
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import argparse
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import json
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import time
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from concurrent.futures import ThreadPoolExecutor
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from functools import partial
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import guidance
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from sglang.test.test_utils import (
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add_common_other_args_and_parse,
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call_generate_outlines,
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)
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from sglang.utils import dump_state_text, read_jsonl
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from tqdm import tqdm
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# there are some FSM bugs with json regex converted from pydantic model
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# here use a string regex instead
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# regex_string = build_regex_from_object(HarryPoterRole)
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character_regex = (
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r"""\{\n"""
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+ r""" "name": "[\w\d\s]{1,16}",\n"""
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+ r""" "house": "(Gryffindor|Slytherin|Ravenclaw|Hufflepuff)",\n"""
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+ r""" "blood status": "(Pure-blood|Half-blood|Muggle-born)",\n"""
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+ r""" "occupation": "(student|teacher|auror|ministry of magic|death eater|order of the phoenix)",\n"""
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+ r""" "wand": \{\n"""
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+ r""" "wood": "[\w\d\s]{1,16}",\n"""
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+ r""" "core": "[\w\d\s]{1,16}",\n"""
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+ r""" "length": [0-9]{1,2}\.[0-9]{0,2}\n"""
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+ r""" \},\n"""
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+ r""" "alive": "(Alive|Deceased)",\n"""
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+ r""" "patronus": "[\w\d\s]{1,16}",\n"""
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+ r""" "bogart": "[\w\d\s]{1,16}"\n"""
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+ r"""\}"""
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)
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city_regex = (
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r"""\{\n"""
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+ r""" "name": "[\w\d\s]{1,16}",\n"""
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+ r""" "country": "[\w\d\s]{1,16}",\n"""
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+ r""" "latitude": [-+]?[0-9]*\.?[0-9]{0,2},\n"""
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+ r""" "population": [-+]?[0-9]{1,9},\n"""
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+ r""" "top 3 landmarks": \["[\w\d\s]{1,16}", "[\w\d\s]{1,16}", "[\w\d\s]{1,16}"\]\n"""
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+ r"""\}"""
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)
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# fmt: off
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def character_gen(name, generate):
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s = name + " is a character in Harry Potter. Please fill in the following information about this character.\n"
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s += generate(s, max_tokens=256, regex=character_regex)
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return s
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# fmt: on
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# fmt: off
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def city_gen(document, generate):
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s = "Please extract the information of a city from the following wikipedia page.\n"
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s += "Page begin.\n" + document + "Page end.\n"
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s += "Here is the name, country, and symbol of the city in JSON format.\n"
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s += generate(s, max_tokens=256, regex=city_regex)
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return s
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# fmt: on
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@guidance
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def character_maker(lm, name):
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regex_str_no_quote = r"[\w\d\s]+"
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regex_float = r"[0-9]+\.[0-9]+"
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lm += f"""\
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{name} is a character in Harry Potter. Please fill in the following information about him/her.
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{{
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"name": "{guidance.gen("name", max_tokens=16, regex=regex_str_no_quote)}",
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"house": "{guidance.select(options=['Gryffindor', 'Slytherin', 'Ravenclaw', 'Hufflepuff'], name='house')}",
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"blood status": "{guidance.select(options=['Pure-blood', 'Half-blood', 'Muggle-born'], name='blood status')}",
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"occupation": "{guidance.select(options=['student', 'teacher', 'auror', 'ministry of magic', 'death eater', 'order of the phoenix'], name='occupation')}",
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"wand": {{
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"wood": "{guidance.gen("wood", max_tokens=16, regex=regex_str_no_quote)}",
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"core": "{guidance.gen('core', max_tokens=16, regex=regex_str_no_quote)}",
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"length": {guidance.gen('length', max_tokens=10, regex=regex_float)}
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}},
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"alive": "{guidance.select(options=['Alive', 'Deceased'], name='alive')}",
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"patronus": "{guidance.gen('patronus', max_tokens=16, regex=regex_str_no_quote)}",
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"bogart": "{guidance.gen('bogart', max_tokens=16, regex=regex_str_no_quote)}"
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}}
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"""
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return lm
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@guidance
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def city_maker(lm, document):
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regex_str_no_quote = r"[\w\d\s]+"
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regex_float = r"[0-9]+\.[0-9]+"
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lm += f"""\
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Please extract the information of a city from the following wikipedia page.
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Page begin.
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{document}
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Page end.
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Here is the name, country, and symbol of the city in JSON format.
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{{
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"name": "{guidance.gen("name", max_tokens=16, regex=regex_str_no_quote)}",
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"country": "{guidance.gen("country", max_tokens=16, regex=regex_str_no_quote)}",
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"latitude": {guidance.gen("latitude", max_tokens=10, regex=regex_float)},
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"population": {guidance.gen("population", max_tokens=10, regex=r"[0-9]+")},
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"top 3 landmarks": [
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"{guidance.gen("landmark1", max_tokens=16, regex=regex_str_no_quote)}", "{guidance.gen("landmark2", max_tokens=16, regex=regex_str_no_quote)}", "{guidance.gen("landmark3", max_tokens=16, regex=regex_str_no_quote)}"
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]
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}}
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"""
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return lm
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def bench_character(args):
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arguments = []
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with open(args.data_path, "r") as f:
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for line in f:
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arguments.append({"name": line.strip()})
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arguments = arguments[: args.num_jsons]
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states = [None] * len(arguments)
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# Select backend
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if args.backend == "vllm":
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url = f"{args.host}:{args.port}/generate"
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generate = partial(call_generate_outlines, url=url, temperature=0)
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def func(i):
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states[i] = character_gen(**arguments[i], generate=generate)
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get_one_answer = func
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elif args.backend == "guidance":
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model = guidance.models.LlamaCpp(
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args.llama_cpp_model_path,
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n_gpu_layers=-1,
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n_ctx=4096,
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)
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def func(i):
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lm = model + character_maker(**arguments[i])
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states[i] = lm
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get_one_answer = func
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else:
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raise ValueError(f"Invalid backend: {args.backend}")
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tic = time.time()
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if args.parallel == 1:
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for i in tqdm(range(len(arguments))):
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get_one_answer(i)
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else:
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with ThreadPoolExecutor(args.parallel) as executor:
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rets = executor.map(get_one_answer, list(range(len(arguments))))
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for _ in rets:
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pass
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latency = time.time() - tic
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return states, latency
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def bench_city_doc(args):
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arguments = []
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for line in read_jsonl(args.data_path):
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arguments.append({"document": line["document"]})
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arguments = arguments[: args.num_jsons]
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states = [None] * len(arguments)
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# Select backend
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if args.backend == "vllm":
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url = f"{args.host}:{args.port}/generate"
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generate = partial(call_generate_outlines, url=url, temperature=0)
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def func(i):
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states[i] = city_gen(**arguments[i], generate=generate)
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get_one_answer = func
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elif args.backend == "guidance":
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model = guidance.models.LlamaCpp(
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args.llama_cpp_model_path,
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n_gpu_layers=-1,
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n_ctx=4096,
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)
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def func(i):
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lm = model + city_maker(**arguments[i])
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states[i] = lm
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get_one_answer = func
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else:
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raise ValueError(f"Invalid backend: {args.backend}")
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tic = time.time()
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if args.parallel == 1:
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for i in tqdm(range(len(arguments))):
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get_one_answer(i)
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else:
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with ThreadPoolExecutor(args.parallel) as executor:
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rets = executor.map(get_one_answer, list(range(len(arguments))))
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for _ in rets:
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pass
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latency = time.time() - tic
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return states, latency
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def main(args):
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if args.mode == "character":
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args.data_path = "dataset.txt"
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states, latency = bench_character(args)
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elif args.mode == "city":
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args.data_path = "questions.jsonl"
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states, latency = bench_city_doc(args)
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# Compute accuracy
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print(f"Latency: {latency:.3f}")
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# Write results
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dump_state_text(f"tmp_output_{args.backend}_{args.mode}.txt", states)
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with open(args.result_file, "a") as fout:
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value = {
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"task": "json_jump_forward",
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"backend": args.backend,
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"latency": round(latency, 3),
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"num_jsons": args.num_jsons,
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"mode": args.mode,
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"parallel": args.parallel,
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}
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fout.write(json.dumps(value) + "\n")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--data-path", type=str)
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parser.add_argument("--num-jsons", type=int, default=50)
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parser.add_argument(
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"--mode", type=str, default="character", choices=["character", "city"]
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)
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parser.add_argument(
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"--llama-cpp-model-path",
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type=str,
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default="/home/ubuntu/model_weights/Llama-2-7b-chat-hf/ggml-model-f16.gguf",
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)
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args = add_common_other_args_and_parse(parser)
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main(args)
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143
benchmark/json_jump_forward/bench_sglang.py
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143
benchmark/json_jump_forward/bench_sglang.py
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@@ -0,0 +1,143 @@
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import argparse
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import json
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import time
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import sglang as sgl
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from sglang.test.test_utils import (
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add_common_sglang_args_and_parse,
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select_sglang_backend,
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)
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from sglang.utils import dump_state_text, read_jsonl
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# there are some FSM bugs with json regex converted from pydantic model
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# here use a string regex instead
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# regex_string = build_regex_from_object(HarryPoterRole)
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character_regex = (
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r"""\{\n"""
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+ r""" "name": "[\w\d\s]{1,16}",\n"""
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+ r""" "house": "(Gryffindor|Slytherin|Ravenclaw|Hufflepuff)",\n"""
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+ r""" "blood status": "(Pure-blood|Half-blood|Muggle-born)",\n"""
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+ r""" "occupation": "(student|teacher|auror|ministry of magic|death eater|order of the phoenix)",\n"""
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+ r""" "wand": \{\n"""
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+ r""" "wood": "[\w\d\s]{1,16}",\n"""
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+ r""" "core": "[\w\d\s]{1,16}",\n"""
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+ r""" "length": [0-9]{1,2}\.[0-9]{0,2}\n"""
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+ r""" \},\n"""
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+ r""" "alive": "(Alive|Deceased)",\n"""
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+ r""" "patronus": "[\w\d\s]{1,16}",\n"""
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+ r""" "bogart": "[\w\d\s]{1,16}"\n"""
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+ r"""\}"""
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)
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city_regex = (
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r"""\{\n"""
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+ r""" "name": "[\w\d\s]{1,16}",\n"""
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+ r""" "country": "[\w\d\s]{1,16}",\n"""
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+ r""" "latitude": [-+]?[0-9]*\.?[0-9]{0,2},\n"""
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+ r""" "population": [-+]?[0-9]{1,9},\n"""
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+ r""" "top 3 landmarks": \["[\w\d\s]{1,16}", "[\w\d\s]{1,16}", "[\w\d\s]{1,16}"\]\n"""
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+ r"""\}"""
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)
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# fmt: off
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@sgl.function
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def character_gen(s, name):
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s += name + " is a character in Harry Potter. Please fill in the following information about this character.\n"
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s += sgl.gen("json_output", max_tokens=256, regex=character_regex)
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# fmt: on
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# fmt: off
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@sgl.function
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def city_gen(s, document):
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s += "Please extract the information of a city from the following wikipedia page.\n"
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s += "Page begin.\n" + document + "Page end.\n"
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s += "Here is the name, country, and symbol of the city in JSON format.\n"
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s += sgl.gen("json_output",max_tokens=256, regex=city_regex)
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# fmt: on
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def bench_city_doc(args):
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arguments = []
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for line in read_jsonl(args.data_path):
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arguments.append({"document": line["document"]})
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arguments = arguments[: args.num_jsons]
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# Select backend
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backend = select_sglang_backend(args)
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sgl.set_default_backend(backend)
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# Run requests
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tic = time.time()
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states = city_gen.run_batch(
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arguments,
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temperature=0,
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num_threads=args.parallel,
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progress_bar=(args.parallel == 1),
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)
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latency = time.time() - tic
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return states, latency
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def bench_character(args):
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arguments = []
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with open(args.data_path, "r") as f:
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for line in f:
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arguments.append({"name": line.strip()})
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arguments = arguments[: args.num_jsons]
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# Select backend
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backend = select_sglang_backend(args)
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sgl.set_default_backend(backend)
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# Run requests
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tic = time.time()
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states = character_gen.run_batch(
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arguments,
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temperature=0,
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num_threads=args.parallel,
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progress_bar=(args.parallel == 1),
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)
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latency = time.time() - tic
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return states, latency
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def main(args):
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if args.mode == "character":
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args.data_path = "dataset.txt"
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states, latency = bench_character(args)
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elif args.mode == "city":
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args.data_path = "questions.jsonl"
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states, latency = bench_city_doc(args)
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# Compute accuracy
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print(f"Latency: {latency:.3f}")
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# Write results
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dump_state_text(f"tmp_output_{args.backend}_{args.mode}.txt", states)
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with open(f"{args.backend}_{args.mode}.json", "w") as fout:
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for state in states:
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fout.write(state["json_output"] + "\n")
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with open(args.result_file, "a") as fout:
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value = {
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"task": "json_jump_forward",
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"backend": args.backend,
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"latency": round(latency, 3),
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"num_jsons": args.num_jsons,
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"mode": args.mode,
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"parallel": args.parallel,
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}
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fout.write(json.dumps(value) + "\n")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--data-path", type=str)
|
||||
parser.add_argument("--num-jsons", type=int, default=50)
|
||||
parser.add_argument(
|
||||
"--mode", type=str, default="character", choices=["character", "city"]
|
||||
)
|
||||
args = add_common_sglang_args_and_parse(parser)
|
||||
main(args)
|
||||
58
benchmark/json_jump_forward/build_dataset.py
Normal file
58
benchmark/json_jump_forward/build_dataset.py
Normal file
@@ -0,0 +1,58 @@
|
||||
import json
|
||||
|
||||
import transformers
|
||||
import wikipedia
|
||||
|
||||
model_path = "meta-llama/Llama-2-7b-chat-hf"
|
||||
t = transformers.AutoTokenizer.from_pretrained(model_path)
|
||||
city_names = [
|
||||
"los angles",
|
||||
"london",
|
||||
"tokyo",
|
||||
"beijing",
|
||||
"singapore",
|
||||
"paris",
|
||||
"dubai",
|
||||
"sydney",
|
||||
"moscow",
|
||||
"rome",
|
||||
"toronto",
|
||||
"rio de janeiro",
|
||||
"istanbul",
|
||||
"berlin",
|
||||
"auckland",
|
||||
"buenos aires",
|
||||
"mexico city",
|
||||
"mumbai",
|
||||
"seoul",
|
||||
"bangkok",
|
||||
"cairo",
|
||||
"athens",
|
||||
"jerusalem",
|
||||
]
|
||||
|
||||
|
||||
def get_content(city_name):
|
||||
content = str(wikipedia.page(city_name).content)
|
||||
content = content.replace("\n\n", "\n")
|
||||
|
||||
tokens = t.encode(content)
|
||||
|
||||
expected_tokens = 3000
|
||||
truncate_len = int((expected_tokens / len(tokens)) * len(content))
|
||||
truncate_content = content[:truncate_len]
|
||||
truncate_tokens = t.encode(truncate_content)
|
||||
|
||||
# Count token
|
||||
print(
|
||||
f"city_name: {city_name}, #tokens: {len(tokens)}, #truncate tokens: {len(truncate_tokens)}"
|
||||
)
|
||||
|
||||
return truncate_content
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
with open("questions.jsonl", "w") as fout:
|
||||
for city_name in city_names:
|
||||
truncate_content = get_content(city_name)
|
||||
fout.write(json.dumps({"document": truncate_content}) + "\n")
|
||||
50
benchmark/json_jump_forward/dataset.txt
Normal file
50
benchmark/json_jump_forward/dataset.txt
Normal file
@@ -0,0 +1,50 @@
|
||||
Harry Potter
|
||||
Hermione Granger
|
||||
Ron Weasley
|
||||
Albus Dumbledore
|
||||
Severus Snape
|
||||
Rubeus Hagrid
|
||||
Draco Malfoy
|
||||
Ginny Weasley
|
||||
Fred Weasley
|
||||
George Weasley
|
||||
Percy Weasley
|
||||
Sirius Black
|
||||
Remus Lupin
|
||||
Neville Longbottom
|
||||
Luna Lovegood
|
||||
Cedric Diggory
|
||||
Cho Chang
|
||||
Lord Voldemort
|
||||
Minerva McGonagall
|
||||
Filius Flitwick
|
||||
Dolores Umbridge
|
||||
Bellatrix Lestrange
|
||||
Lucius Malfoy
|
||||
Molly Weasley
|
||||
Arthur Weasley
|
||||
Nymphadora Tonks
|
||||
Dobby
|
||||
Moaning Myrtle
|
||||
Peter Pettigrew
|
||||
Alastor 'Mad-Eye' Moody
|
||||
Horace Slughorn
|
||||
Vernon Dursley
|
||||
Petunia Dursley
|
||||
Dudley Dursley
|
||||
Argus Filch
|
||||
Sybill Trelawney
|
||||
Gilderoy Lockhart
|
||||
Fleur Delacour
|
||||
Viktor Krum
|
||||
Bill Weasley
|
||||
Oliver Wood
|
||||
Cornelius Fudge
|
||||
Barty Crouch Sr.
|
||||
Barty Crouch Jr.
|
||||
Kingsley Shacklebolt
|
||||
Quirinus Quirrell
|
||||
Nearly Headless Nick
|
||||
Aunt Marge
|
||||
Griphook
|
||||
Ludo Bagman
|
||||
Reference in New Issue
Block a user