289 lines
9.3 KiB
Python
289 lines
9.3 KiB
Python
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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 tqdm import tqdm
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from sglang.test.test_utils import add_common_other_args_and_parse, get_call_generate
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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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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 this character.
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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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async def call_generate_lmql(
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prompt, temperature, max_tokens, regex, max_len=4096, model=None, **kwargs
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):
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assert model is not None
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import lmql
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@lmql.query(model=model)
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async def program(question, max_tokens, regex):
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'''lmql
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"""{question}[ANSWER]""" where len(TOKENS(ANSWER)) < max_tokens and REGEX(ANSWER, regex)
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return ANSWER
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'''
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return await program(
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question=prompt,
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temperature=temperature,
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max_tokens=max_tokens,
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max_len=max_len,
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regex=regex,
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**kwargs,
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)
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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 == "outlines":
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call_generate = partial(get_call_generate(args), temperature=0)
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def get_one_answer(i):
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states[i] = character_gen(**arguments[i], generate=call_generate)
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elif args.backend == "guidance":
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model = guidance.models.LlamaCpp(
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args.model_path,
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n_gpu_layers=-1,
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n_ctx=args.n_ctx,
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)
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def get_one_answer(i):
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lm = model + character_maker(**arguments[i])
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states[i] = lm
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elif args.backend == "lmql":
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import asyncio
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import lmql
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model = lmql.model(args.model_path, endpoint=f"{args.host}:{args.port}")
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call_generate = partial(
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call_generate_lmql,
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model=model,
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max_tokens=256,
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regex=character_regex,
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)
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async def get_one_answer_async(i):
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states[i] = await call_generate(prompt=arguments[i]["name"], temperature=0)
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else:
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raise ValueError(f"Invalid backend: {args.backend}")
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tic = time.perf_counter()
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if args.backend != "lmql":
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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 = list(
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tqdm(
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executor.map(get_one_answer, list(range(len(arguments)))),
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total=len(arguments),
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)
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)
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for _ in rets:
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pass
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else:
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batches = []
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for i in range(0, len(arguments), args.parallel):
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batches.append(list(range(i, min(i + args.parallel, len(arguments)))))
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loop = asyncio.get_event_loop()
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for bt in tqdm(batches):
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loop.run_until_complete(
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asyncio.gather(*[get_one_answer_async(i) for i in bt])
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)
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latency = time.perf_counter() - 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 == "outlines":
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call_generate = partial(get_call_generate(args), temperature=0)
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def get_one_answer(i):
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states[i] = city_gen(**arguments[i], generate=call_generate)
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elif args.backend == "guidance":
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model = guidance.models.LlamaCpp(
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args.model_path,
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n_gpu_layers=-1,
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n_ctx=args.n_ctx,
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)
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def get_one_answer(i):
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lm = model + city_maker(**arguments[i])
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states[i] = lm
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else:
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raise ValueError(f"Invalid backend: {args.backend}")
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tic = time.perf_counter()
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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.perf_counter() - 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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args = add_common_other_args_and_parse(parser)
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main(args)
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