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Update benchmark scripts (#8)

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Lianmin Zheng
2024-01-15 16:12:57 -08:00
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parent 01ca82d765
commit 70359bf31a
28 changed files with 183 additions and 50 deletions
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benchmark/tree_of_thought_v0/README.md Normal file
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## Download data
```
wget https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data/test.jsonl
```
## Run benchmark
### Benchmark sglang
```
python -m sglang.launch_server --model-path meta-llama/Llama-2-7b-chat-hf --port 30000
```
```
python3 bench_sglang.py --num-questions 32 --parallel 16
python3 bench_sglang.py --num-questions 10 --parallel 1
```
### Benchmark vllm
```
python3 -m vllm.entrypoints.api_server --tokenizer-mode auto --model meta-llama/Llama-2-7b-chat-hf --disable-log-requests --port 21000
```
```
python3 bench_other.py --num-questions 32 --backend vllm
```
### Benchmark lightllm
```
# A10G
python -m lightllm.server.api_server --tokenizer_mode auto --model_dir ~/model_weights/llama-2-7b-chat-hf --max_total_token_num 16000 --port 22000
```
```
python3 bench_other.py --num-questions 32 --backend lightllm
```
### Benchmark guidance
```
python3 bench_other.py --num-questions 32 --backend guidance --parallel 1
```

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benchmark/tree_of_thought_v0/bench_other.py Normal file
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import argparse
import ast
import asyncio
from collections import Counter
from concurrent.futures import ThreadPoolExecutor
from functools import partial
import json
import re
import time
import numpy as np
from tqdm import tqdm
from sglang.test.test_utils import add_common_other_args_and_parse, call_generate_lightllm, call_generate_vllm, call_generate_srt_raw
from sglang.utils import read_jsonl, dump_state_text
INVALID = -9999999
def get_answer_value(answer_str):
answer_str = answer_str.replace(",", "")
numbers = re.findall(r'\d+', answer_str)
if len(numbers) < 1:
return INVALID
try:
return ast.literal_eval(numbers[-1])
except SyntaxError:
return INVALID
def most_frequent_number(numbers):
if not numbers:
return None
frequency = Counter(numbers)
most_frequent = max(frequency, key=frequency.get)
return most_frequent
USER_PREFIX = "[INST] "
USER_SUFFIX = " [/INST]"
ASSISTANT_PREFIX = ""
ASSISTANT_SUFFIX = " </s><s>"
# Use a low temp to make the results more deterministic and the comparison more fair.
temp = 0.3
def propose_plan(s, question, num_branches, call_generate):
s += (USER_PREFIX +
"""Please generate a high-level plan for solving the following question. As the first step, just say what method and idea you will use to solve the question. You can reorganize the information in the question. Do not do the actual calculation. Keep your response concise and within 80 words. Question: """ + question + USER_SUFFIX)
s += ASSISTANT_PREFIX
comps = call_generate(s, max_tokens=256, temperature=temp, stop=None, n=num_branches)
return [s + comp + ASSISTANT_SUFFIX for comp in comps]
def execute_plan(s, num_branches, call_generate):
s += (USER_PREFIX +
"""The plan looks good! Now, use real numbers and do the calculation. Please solve the question step-by-step according to the high-level plan. Give me the final answer. Make your response short.""" + USER_SUFFIX)
s += ASSISTANT_PREFIX
comps = call_generate(s, max_tokens=256, temperature=temp, stop=None, n=num_branches)
return [s + comp + ASSISTANT_SUFFIX for comp in comps]
def reflect_solution(s, num_branches, call_generate):
s += (USER_PREFIX +
"""Okay. Now you evaluate your own solution and give it a score on a scale of 1 to 5. Please do rigorous check of the correctness.""" + USER_SUFFIX)
s += ASSISTANT_PREFIX
comps = call_generate(s, max_tokens=256, temperature=temp, stop=None, n=num_branches)
return [s + comp + ASSISTANT_SUFFIX for comp in comps]
def tree_search(question, num_branches, call_generate):
s = ""
solutions = []
plan_forks = propose_plan(s, question, num_branches, call_generate)
for plan in plan_forks:
sol_forks = execute_plan(plan, num_branches, call_generate)
for sol in sol_forks:
score_forks = reflect_solution(sol, num_branches, call_generate)
solutions.append(sol_forks)
return solutions
def main(args):
lines = read_jsonl(args.data_path)
# Construct prompts
num_branches = 3
questions = []
labels = []
for i in range(len(lines[:args.num_questions])):
questions.append(lines[i]["question"])
labels.append(get_answer_value(lines[i]["answer"]))
assert all(l != INVALID for l in labels)
arguments = [{"question": q, "num_branches": num_branches} for q in questions]
# Select backend
if args.backend == "lightllm":
url = f"{args.host}:{args.port}/generate"
call_generate = partial(call_generate_lightllm, url=url)
elif args.backend == "vllm":
url = f"{args.host}:{args.port}/generate"
call_generate = partial(call_generate_vllm, url=url)
elif args.backend == "srt-raw":
url = f"{args.host}:{args.port}/generate"
call_generate = partial(call_generate_srt_raw, url=url)
elif args.backend == "guidance":
from guidance import models, gen
model = models.LlamaCpp("/home/ubuntu/model_weights/Llama-2-7b-chat.gguf", n_gpu_layers=-1, n_ctx=4096)
def call_generate(prompt, temperature, max_tokens, stop, n):
if n == 1:
out = model + prompt + gen(name="answer",
max_tokens=max_tokens, temperature=temperature, stop=stop)
return out["answer"]
else:
rets = []
for i in range(n):
out = model + prompt + gen(name="answer",
max_tokens=max_tokens, temperature=temperature, stop=stop)
rets.append(out["answer"])
return rets
# Run requests
states = [None] * len(questions)
def get_one_answer(i):
states[i] = tree_search(**arguments[i], call_generate=call_generate)
tic = time.time()
if args.parallel == 1:
for i in tqdm(range(len(questions))):
get_one_answer(i)
else:
with ThreadPoolExecutor(args.parallel) as executor:
executor.map(get_one_answer, list(range(len(questions))))
latency = time.time() - tic
answers_text = []
for s in states:
answers_text.append([x for xs in s for x in xs])
preds = []
for i in range(len(states)):
answers = [get_answer_value(v) for v in answers_text[i]]
preds.append(most_frequent_number(answers))
# Compute accuracy
acc = np.mean(np.array(preds) == np.array(labels))
invalid = np.mean(np.array(preds) == INVALID)
print(f"Latency: {latency:.3f}")
print(f"Invalid: {invalid:.3f}")
print(f"Accuracy: {acc:.3f}")
# Write results
dump_state_text(f"tmp_output_{args.backend}.txt", answers_text)
with open(args.result_file, "a") as fout:
value = {
"task": "tree_of_thought_gsm8k",
"backend": args.backend,
"num_gpus": 1,
"latency": round(latency, 3),
"accuracy": round(acc, 3),
"num_requests": args.num_questions,
"other": {
"num_questions": args.num_questions,
"parallel": args.parallel,
}
}
fout.write(json.dumps(value) + "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--data-path", type=str, default="test.jsonl")
parser.add_argument("--num-questions", type=int, default=200)
args = add_common_other_args_and_parse(parser)
main(args)

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benchmark/tree_of_thought_v0/bench_sglang.py Normal file
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import argparse
import ast
from collections import Counter
import json
import re
import time
import numpy as np
from sglang.test.test_utils import add_common_sglang_args_and_parse, select_sglang_backend
from sglang.utils import read_jsonl, dump_state_text
import sglang as sgl
INVALID = -9999999
def get_answer_value(answer_str):
answer_str = answer_str.replace(",", "")
numbers = re.findall(r'\d+', answer_str)
if len(numbers) < 1:
return INVALID
try:
return ast.literal_eval(numbers[-1])
except SyntaxError:
return INVALID
def most_frequent_number(numbers):
if not numbers:
return None
frequency = Counter(numbers)
most_frequent = max(frequency, key=frequency.get)
return most_frequent
# Use a low temp to make the results more deterministic and the comparison more fair.
temp = 0.3
def propose_plan(s, question, num_branches):
s += sgl.user(
"""Please generate a high-level plan for solving the following question. As the first step, just say what method and idea you will use to solve the question. You can reorganize the information in the question. Do not do the actual calculation. Keep your response concise and within 80 words. Question: """ + question)
forks = s.fork(num_branches)
forks += sgl.assistant(sgl.gen("plan", max_tokens=256, temperature=temp))
return forks
def execute_plan(s, num_branches):
s += sgl.user(
"""The plan looks good! Now, use real numbers and do the calculation. Please solve the question step-by-step according to the high-level plan. Give me the final answer. Make your response short.""")
forks = s.fork(num_branches)
forks += sgl.assistant(sgl.gen("answer", max_tokens=256, temperature=temp))
return forks
def reflect_solution(s, num_branches):
s += sgl.user(
"""Okay. Now you evaluate your own solution and give it a score on a scale of 1 to 5. Please do rigorous check of the correctness.""")
forks = s.fork(num_branches)
forks += sgl.assistant(sgl.gen("score", max_tokens=256, temperature=temp))
return forks
@sgl.function
def tree_search(s, question, num_branches):
forks_to_join = []
plan_forks = propose_plan(s, question, num_branches)
forks_to_join.append(plan_forks)
sol_states = []
for plan in plan_forks:
forks = execute_plan(plan, num_branches)
forks_to_join.append(forks)
sol_states.extend(forks)
for sol in sol_states:
forks = reflect_solution(sol, num_branches)
forks_to_join.append(forks)
for f in reversed(forks_to_join):
f.join()
def main(args):
lines = read_jsonl(args.data_path)
# Construct prompts
num_branches = 3
questions = []
labels = []
for i in range(len(lines[:args.num_questions])):
questions.append(lines[i]["question"])
labels.append(get_answer_value(lines[i]["answer"]))
assert all(l != INVALID for l in labels)
arguments = [{"question": q, "num_branches": num_branches} for q in questions]
# Select backend
backend = select_sglang_backend(args)
# Run requests
tic = time.time()
states = tree_search.run_batch(
arguments, temperature=0, backend=backend, num_threads=args.parallel)
latency = time.time() - tic
answers_text = []
for s in states:
answers_text.append([x for xs in s["answer"] for x in xs])
preds = []
for i in range(len(states)):
answers = [get_answer_value(v) for v in answers_text[i]]
preds.append(most_frequent_number(answers))
# Compute accuracy
acc = np.mean(np.array(preds) == np.array(labels))
invalid = np.mean(np.array(preds) == INVALID)
print(f"Latency: {latency:.3f}")
print(f"Invalid: {invalid:.3f}")
print(f"Accuracy: {acc:.3f}")
# Write results
dump_state_text(f"tmp_output_{args.backend}.txt", answers_text)
with open(args.result_file, "a") as fout:
value = {
"task": "tree_of_thought_gsm8k",
"backend": args.backend,
"num_gpus": 1,
"latency": round(latency, 3),
"accuracy": round(acc, 3),
"num_requests": args.num_questions,
"other": {
"num_questions": args.num_questions,
"parallel": args.parallel,
}
}
fout.write(json.dumps(value) + "\n")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--data-path", type=str, default="test.jsonl")
parser.add_argument("--num-questions", type=int, default=200)
args = add_common_sglang_args_and_parse(parser)
main(args)