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Unverified Commit 311490a7 authored by Woosuk Kwon's avatar Woosuk Kwon Committed by GitHub
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Add script for benchmarking serving throughput (#145)

parent da5ddcd5
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Showing with 421 additions and 415 deletions
benchmarks/benchmark_async_llm_server.py
View file @ 311490a7
...@@ -10,6 +10,7 @@ def main(args: argparse.Namespace): ...@@ -10,6 +10,7 @@ def main(args: argparse.Namespace):
prompts = [f"Tell me a story with more than {''.join([str(i+1)] * 5)} words" prompts = [f"Tell me a story with more than {''.join([str(i+1)] * 5)} words"
for i in range(args.n_threads)] for i in range(args.n_threads)]
api_url = f"http://{args.host}:{args.port}/generate"
headers = {"User-Agent": "CacheFlow Benchmark Client"} headers = {"User-Agent": "CacheFlow Benchmark Client"}
ploads = [{ ploads = [{
"prompt": p, "prompt": p,
...@@ -19,8 +20,8 @@ def main(args: argparse.Namespace): ...@@ -19,8 +20,8 @@ def main(args: argparse.Namespace):
} for p in prompts] } for p in prompts]
def send_request(results, i): def send_request(results, i):
response = requests.post(args.api_url, headers=headers, response = requests.post(api_url, headers=headers, json=ploads[i],
json=ploads[i], stream=True) stream=True)
results[i] = response results[i] = response
# use args.n_threads to prompt the backend # use args.n_threads to prompt the backend
...@@ -50,7 +51,8 @@ def main(args: argparse.Namespace): ...@@ -50,7 +51,8 @@ def main(args: argparse.Namespace):
if __name__ == "__main__": if __name__ == "__main__":
parser = argparse.ArgumentParser() parser = argparse.ArgumentParser()
parser.add_argument("--api-url", type=str, default="http://localhost:8001/generate") parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--port", type=int, default=8001)
parser.add_argument("--max-tokens", type=int, default=128) parser.add_argument("--max-tokens", type=int, default=128)
parser.add_argument("--n-threads", type=int, default=128) parser.add_argument("--n-threads", type=int, default=128)
args = parser.parse_args() args = parser.parse_args()
......
benchmarks/benchmark_latency.py
View file @ 311490a7
"""Benchmark the latency of processing a single batch of requests."""
import argparse import argparse
import time import time
......
benchmarks/benchmark_serving.py 0 → 100644
View file @ 311490a7
"""Benchmark online serving throughput.
On the server side, run one of the following commands:
(CacheFlow backend)
python -m cacheflow.entrypoints.simple_fastapi_frontend \
--disable-log-requests --model <your_model>
(TGI backend)
./launch_hf_server.sh <your_model>
On the client side, run:
python benchmarks/benchmark_serving.py \
--backend <backend> \
--tokenizer <your_model> --dataset <target_dataset> \
--request-rate <request_rate>
"""
import argparse
import asyncio
import json
import random
import time
from typing import AsyncGenerator, List, Tuple
import aiohttp
import numpy as np
from transformers import AutoConfig, AutoTokenizer, PreTrainedTokenizerBase
# (prompt len, output len, latency)
REQUEST_LATENCY: List[Tuple[int, int, float]] = []
def get_tokenizer(model_name: str) -> PreTrainedTokenizerBase:
config = AutoConfig.from_pretrained(model_name)
if config.model_type == "llama":
# A workaround for potential protobuf errors.
model_name = "hf-internal-testing/llama-tokenizer"
return AutoTokenizer.from_pretrained(model_name)
def sample_requests(
dataset_path: str,
num_requests: int,
tokenizer: PreTrainedTokenizerBase,
) -> List[Tuple[str, int, int]]:
# 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
]
# Tokenize the prompts and completions.
prompts = [prompt for prompt, _ in dataset]
prompt_token_ids = tokenizer(prompts).input_ids
completions = [completion for _, completion in dataset]
completion_token_ids = tokenizer(completions).input_ids
tokenized_dataset = []
for i in range(len(dataset)):
output_len = len(completion_token_ids[i])
tokenized_dataset.append((prompts[i], prompt_token_ids[i], output_len))
# Filter out too long sequences.
filtered_dataset: List[Tuple[str, int, int]] = []
for prompt, prompt_token_ids, output_len in tokenized_dataset:
prompt_len = len(prompt_token_ids)
if prompt_len < 4 or output_len < 4:
# Prune too short sequences.
# This is because TGI causes errors when the input or output length
# is too short.
continue
if prompt_len > 1024 or prompt_len + output_len > 2048:
# Prune too long sequences.
continue
filtered_dataset.append((prompt, prompt_len, output_len))
# Sample the requests.
sampled_requests = random.sample(filtered_dataset, num_requests)
return sampled_requests
async def get_request(
input_requests: List[Tuple[str, int, int]],
request_rate: float,
) -> AsyncGenerator[Tuple[str, int, int], None]:
input_requests = iter(input_requests)
for request in input_requests:
yield request
if request_rate == float("inf"):
# If the request rate is infinity, then we don't need to wait.
continue
# Sample the request interval from the exponential distribution.
interval = np.random.exponential(1.0 / request_rate)
# The next request will be sent after the interval.
await asyncio.sleep(interval)
async def send_request(
backend: str,
api_url: str,
prompt: str,
prompt_len: int,
output_len: int,
best_of: int,
use_beam_search: bool,
) -> None:
request_start_time = time.time()
headers = {"User-Agent": "Benchmark Client"}
if backend == "cacheflow":
pload = {
"prompt": prompt,
"n": 1,
"best_of": best_of,
"use_beam_search": use_beam_search,
"temperature": 0.0 if use_beam_search else 1.0,
"top_p": 1.0,
"max_tokens": output_len,
"ignore_eos": True,
"stream": False,
}
elif backend == "tgi":
assert not use_beam_search
params = {
"best_of": best_of,
"max_new_tokens": output_len,
"do_sample": True,
}
pload = {
"inputs": prompt,
"parameters": params,
}
else:
raise ValueError(f"Unknown backend: {backend}")
timeout = aiohttp.ClientTimeout(total=3 * 3600)
async with aiohttp.ClientSession(timeout=timeout) as session:
while True:
async with session.post(api_url, headers=headers, json=pload) as response:
chunks = []
async for chunk, _ in response.content.iter_chunks():
chunks.append(chunk)
output = b"".join(chunks).decode("utf-8")
output = json.loads(output)
# Re-send the request if it failed.
if "error" not in output:
break
request_end_time = time.time()
request_latency = request_end_time - request_start_time
REQUEST_LATENCY.append((prompt_len, output_len, request_latency))
async def benchmark(
backend: str,
api_url: str,
input_requests: List[Tuple[str, int, int]],
best_of: int,
use_beam_search: bool,
request_rate: float,
) -> None:
tasks: List[asyncio.Task] = []
async for request in get_request(input_requests, request_rate):
prompt, prompt_len, output_len = request
task = asyncio.create_task(send_request(backend, api_url, prompt,
prompt_len, output_len,
best_of, use_beam_search))
tasks.append(task)
await asyncio.gather(*tasks)
def main(args: argparse.Namespace):
print(args)
random.seed(args.seed)
np.random.seed(args.seed)
api_url = f"http://{args.host}:{args.port}/generate"
tokenizer = get_tokenizer(args.tokenizer)
input_requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
benchmark_start_time = time.time()
asyncio.run(benchmark(args.backend, api_url, input_requests, args.best_of,
args.use_beam_search, args.request_rate))
benchmark_end_time = time.time()
benchmark_time = benchmark_end_time - benchmark_start_time
print(f"Total time: {benchmark_time:.2f} s")
print(f"Throughput: {args.num_prompts / benchmark_time:.2f} requests/s")
# Compute the latency statistics.
avg_latency = np.mean([latency for _, _, latency in REQUEST_LATENCY])
print(f"Average latency: {avg_latency:.2f} s")
avg_per_token_latency = np.mean([
latency / (prompt_len + output_len)
for prompt_len, output_len, latency in REQUEST_LATENCY
])
print(f"Average latency per token: {avg_per_token_latency:.2f} s")
avg_per_output_token_latency = np.mean([
latency / output_len
for _, output_len, latency in REQUEST_LATENCY
])
print("Average latency per output token: "
f"{avg_per_output_token_latency:.2f} s")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Benchmark the online serving throughput.")
parser.add_argument("--backend", type=str, default="cacheflow",
choices=["cacheflow", "tgi"])
parser.add_argument("--host", type=str, default="localhost")
parser.add_argument("--port", type=int, default=8001)
parser.add_argument("--dataset", type=str, required=True,
help="Path to the dataset.")
parser.add_argument("--tokenizer", type=str, required=True,
help="Name or path of the tokenizer.")
parser.add_argument("--best-of", type=int, default=1,
help="Generates `best_of` sequences per prompt and "
"returns the best one.")
parser.add_argument("--use-beam-search", action="store_true")
parser.add_argument("--num-prompts", type=int, default=1000,
help="Number of prompts to process.")
parser.add_argument("--request-rate", type=float, default=float("inf"),
help="Number of requests per second. If this is inf, "
"then all the requests are sent at time 0. "
"Otherwise, we use Poisson process to synthesize "
"the request arrival times.")
parser.add_argument("--seed", type=int, default=0)
args = parser.parse_args()
main(args)
benchmarks/benchmark_text_completion.py deleted 100644 → 0
View file @ da5ddcd5
import argparse
import logging
import os
import pickle
import time
from typing import List
from tqdm import tqdm
from transformers import AutoConfig
from benchmark.trace import generate_text_completion_requests
from cacheflow.master.server import (
add_server_arguments, process_server_arguments,
init_local_server_and_frontend_with_arguments)
from cacheflow.sampling_params import SamplingParams
logger = logging.getLogger(__name__)
def main(args: argparse.Namespace):
server, frontend = init_local_server_and_frontend_with_arguments(args)
# Generate requests.
requests = generate_text_completion_requests(
args.dataset,
args.request_rate,
args.duration,
args.seed,
args.n1,
args.n2,
args.n3,
args.n4,
args.n6,
args.n2_beam,
args.n4_beam,
args.n6_beam,
args.n8_beam,
)
# Warm up.
logger.info('Warming up.')
num_warmup_requests = 8
warmup_input_len = 8
warmup_output_len = 32
warmup_sampling_params = SamplingParams(
n=1,
temperature=1.0,
top_p=0.99,
max_num_steps=warmup_output_len,
use_beam_search=False,
stop_token_ids=set(),
num_logprobs=0,
context_window_size=None,
)
for _ in range(num_warmup_requests):
frontend._add_query([0] * warmup_input_len, warmup_sampling_params)
server.add_sequence_groups(frontend.get_inputs())
while True:
server.step()
if not server.has_unfinished_requests():
break
# Start benchmarking.
logger.info('Start benchmarking.')
# Initialize tqdm.
pbar = tqdm(total=len(requests), desc='Finished requests')
finished = []
server.scheduler.reset_stats()
start_time = time.time()
while True:
now = time.time()
if args.timeout is not None and now - start_time > args.timeout:
logger.info('Timeout. Stop benchmarking.')
break
while requests:
if requests[0][0] <= now - start_time:
request_time, input_tokens, sampling_params = requests.pop(0)
frontend._add_query(
input_tokens, sampling_params, arrival_time=start_time + request_time)
else:
break
server.add_sequence_groups(frontend.get_inputs())
updated_seq_groups = server.step()
now = time.time()
for seq_group in updated_seq_groups:
if not seq_group.is_finished():
continue
arrival_time = seq_group.arrival_time
finish_time = now
for seq in seq_group.get_seqs():
seq_len = seq.get_len()
output_len = seq_len - seq.prompt_len
finished.append({
'group_id': seq_group.group_id,
'seq_id': seq.seq_id,
'arrival_time': arrival_time,
'finish_time': finish_time,
'prompt_len': seq.prompt_len,
'output_len': output_len,
})
pbar.update(1)
if not (requests or server.has_unfinished_requests()):
break
pbar.close()
logger.info('Finish benchmarking. Saving stats.')
server.scheduler.save_stats(args.output_dir)
with open(os.path.join(args.output_dir, 'sequences.pkl'), 'wb') as f:
pickle.dump(finished, f)
logger.info('Done.')
def get_model_name(model: str) -> str:
OPT_MODELS = [
'opt-125m',
'opt-350m',
'opt-1.3b',
'opt-2.7b',
'opt-6.7b',
'opt-13b',
'opt-30b',
'opt-66b',
'opt-175b',
]
for opt_model in OPT_MODELS:
if opt_model in model:
return opt_model
config = AutoConfig.from_pretrained(model)
assert config.model_type == 'llama'
hidden_size = config.hidden_size
if hidden_size == 4096:
return 'llama-7b'
elif hidden_size == 5120:
return 'llama-13b'
elif hidden_size == 6656:
return 'llama-30b'
elif hidden_size == 8192:
return 'llama-65b'
else:
raise ValueError(f'Unknown model: {model}')
def get_dataset_name(dataset: str) -> str:
if 'sharegpt' in dataset.lower():
return 'sharegpt'
elif 'alpaca' in dataset.lower():
return 'alpaca'
else:
raise ValueError(f'Unknown dataset: {dataset}')
def get_sampling_dir_name(
n1: float,
n2: float,
n3: float,
n4: float,
n6: float,
n2_beam: float,
n4_beam: float,
n6_beam: float,
n8_beam: float,
) -> str:
method = ''
if n1 > 0.0:
method = 'n1' if n1 == 1.0 else method + f'n1-{n1}-'
if n2 > 0.0:
method = 'n2' if n2 == 1.0 else method + f'n2-{n2}-'
if n3 > 0.0:
method = 'n3' if n3 == 1.0 else method + f'n3-{n3}-'
if n4 > 0.0:
method = 'n4' if n4 == 1.0 else method + f'n4-{n4}-'
if n6 > 0.0:
method = 'n6' if n6 == 1.0 else method + f'n6-{n6}-'
if n2_beam > 0.0:
method = 'n2-beam' if n2_beam == 1.0 else method + f'n2-beam-{n2_beam}-'
if n4_beam > 0.0:
method = 'n4-beam' if n4_beam == 1.0 else method + f'n4-beam-{n4_beam}-'
if n6_beam > 0.0:
method = 'n6-beam' if n6_beam == 1.0 else method + f'n6-beam-{n6_beam}-'
if n8_beam > 0.0:
method = 'n8-beam' if n8_beam == 1.0 else method + f'n8-beam-{n8_beam}-'
return method[:-1] if method.endswith('-') else method
if __name__ == '__main__':
parser = argparse.ArgumentParser(
description='Benchmark the performance on a series of requests.')
parser = add_server_arguments(parser)
parser.add_argument('--output-dir', type=str, help='path to output directory', default=None)
parser.add_argument('--dataset', type=str, help='path to dataset', required=True)
parser.add_argument('--request-rate', type=float, help='reqs/sec', required=True)
parser.add_argument('--duration', type=int, help='duration in seconds', required=True)
parser.add_argument('--do-memory-analysis', action='store_true',
help='do memory analysis (This will lower the throughput. Use this only for analysis.)')
parser.add_argument('--timeout', type=int, help='time out in seconds', default=None)
parser.add_argument('--n1', type=float, help='ratio of requests with n=1', default=0.0)
parser.add_argument('--n2', type=float, help='ratio of requests with n=2', default=0.0)
parser.add_argument('--n3', type=float, help='ratio of requests with n=3', default=0.0)
parser.add_argument('--n4', type=float, help='ratio of requests with n=4', default=0.0)
parser.add_argument('--n6', type=float, help='ratio of requests with n=6', default=0.0)
parser.add_argument('--n2-beam', type=float, help='ratio of requests with n=2 & beam search', default=0.0)
parser.add_argument('--n4-beam', type=float, help='ratio of requests with n=4 & beam search', default=0.0)
parser.add_argument('--n6-beam', type=float, help='ratio of requests with n=6 & beam search', default=0.0)
parser.add_argument('--n8-beam', type=float, help='ratio of requests with n=8 & beam search', default=0.0)
args = parser.parse_args()
args = process_server_arguments(args)
if args.n1 + args.n2 + args.n3 + args.n4 + args.n6 + args.n2_beam + args.n4_beam + args.n6_beam + args.n8_beam != 1.0:
raise ValueError('The ratios of requests must sum to 1.')
model_name = get_model_name(args.model)
dataset_name = get_dataset_name(args.dataset)
if 'opt' in model_name:
if 'opt' not in args.dataset.lower():
raise ValueError(f'OPT models can only be used with OPT datasets.')
elif 'llama' in model_name:
if 'llama' not in args.dataset.lower():
raise ValueError(f'Llama models can only be used with Llama datasets.')
dataset_name = 'sharegpt' if 'sharegpt' in args.dataset else 'alpaca'
sample_dir = get_sampling_dir_name(
args.n1, args.n2, args.n3, args.n4, args.n6, args.n2_beam, args.n4_beam, args.n6_beam, args.n8_beam)
if args.output_dir is None:
args.output_dir = os.path.join(
'../exp',
dataset_name,
f'{model_name}-tp{args.tensor_parallel_size}',
sample_dir,
'cacheflow',
f'block{args.block_size}',
f'req-rate-{args.request_rate}',
f'seed{args.seed}',
f'duration-{args.duration}',
)
os.makedirs(args.output_dir, exist_ok=True)
# Set up logging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
handlers=[
logging.StreamHandler(),
logging.FileHandler(os.path.join(args.output_dir, 'log.txt')),
],
)
logger.info(args)
main(args)
benchmarks/benchmark_throughput.py
View file @ 311490a7
"""Benchmark offline inference throughput."""
import argparse import argparse
import json import json
import random import random
...@@ -5,14 +6,29 @@ import time ...@@ -5,14 +6,29 @@ import time
from typing import List, Tuple from typing import List, Tuple
from cacheflow import LLM, SamplingParams from cacheflow import LLM, SamplingParams
from transformers import PreTrainedTokenizerBase import torch
from transformers import (AutoConfig, AutoTokenizer, AutoModelForCausalLM,
PreTrainedTokenizerBase)
from tqdm import tqdm
def get_tokenizer(model_name: str) -> PreTrainedTokenizerBase:
config = AutoConfig.from_pretrained(model_name)
if config.model_type == "llama":
# A workaround for potential protobuf errors.
model_name = "hf-internal-testing/llama-tokenizer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
# To enable padding in the HF backend.
tokenizer.pad_token = tokenizer.eos_token
return tokenizer
return AutoTokenizer.from_pretrained(model_name)
def sample_requests( def sample_requests(
dataset_path: str, dataset_path: str,
num_requests: int, num_requests: int,
tokenizer: PreTrainedTokenizerBase, tokenizer: PreTrainedTokenizerBase,
) -> List[Tuple[List[int], int]]: ) -> List[Tuple[str, int, int]]:
# Load the dataset. # Load the dataset.
with open(dataset_path) as f: with open(dataset_path) as f:
dataset = json.load(f) dataset = json.load(f)
...@@ -35,45 +51,52 @@ def sample_requests( ...@@ -35,45 +51,52 @@ def sample_requests(
tokenized_dataset = [] tokenized_dataset = []
for i in range(len(dataset)): for i in range(len(dataset)):
output_len = len(completion_token_ids[i]) output_len = len(completion_token_ids[i])
tokenized_dataset.append((prompt_token_ids[i], output_len)) tokenized_dataset.append((prompts[i], prompt_token_ids[i], output_len))
# Filter out if the prompt length + output length is greater than 2048.
tokenized_dataset = [ # Filter out too long sequences.
(prompt_token_ids, output_len) filtered_dataset: List[Tuple[str, int, int]] = []
for prompt_token_ids, output_len in tokenized_dataset for prompt, prompt_token_ids, output_len in tokenized_dataset:
if len(prompt_token_ids) + output_len <= 2048 prompt_len = len(prompt_token_ids)
] if prompt_len < 4 or output_len < 4:
# Prune too short sequences.
continue
if prompt_len > 1024 or prompt_len + output_len > 2048:
# Prune too long sequences.
continue
filtered_dataset.append((prompt, prompt_len, output_len))
# Sample the requests. # Sample the requests.
sampled_requests = random.sample(tokenized_dataset, num_requests) sampled_requests = random.sample(filtered_dataset, num_requests)
return sampled_requests return sampled_requests
def main(args: argparse.Namespace): def run_cacheflow(
print(args) requests: List[Tuple[str, int, int]],
random.seed(args.seed) model: str,
tensor_parallel_size: int,
seed: int,
n: int,
use_beam_search: bool,
) -> float:
llm = LLM( llm = LLM(
model=args.model, model=model,
tensor_parallel_size=args.tensor_parallel_size, tensor_parallel_size=tensor_parallel_size,
seed=args.seed, seed=seed,
) )
tokenizer = llm.get_tokenizer()
requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
# Add the requests to the server. # Add the requests to the server.
for prompt_token_ids, output_len in requests: for prompt, _, output_len in requests:
sampling_params = SamplingParams( sampling_params = SamplingParams(
n=args.n, n=n,
temperature=0.0 if args.use_beam_search else 1.0, temperature=0.0 if use_beam_search else 1.0,
top_p=1.0, top_p=1.0,
use_beam_search=args.use_beam_search, use_beam_search=use_beam_search,
ignore_eos=True, ignore_eos=True,
max_tokens=output_len, max_tokens=output_len,
) )
# FIXME(woosuk): Do not use internal method. # FIXME(woosuk): Do not use internal method.
llm._add_request( llm._add_request(
prompt=None, prompt=prompt,
prompt_token_ids=prompt_token_ids,
sampling_params=sampling_params, sampling_params=sampling_params,
) )
...@@ -81,17 +104,95 @@ def main(args: argparse.Namespace): ...@@ -81,17 +104,95 @@ def main(args: argparse.Namespace):
# FIXME(woosuk): Do use internal method. # FIXME(woosuk): Do use internal method.
llm._run_server(use_tqdm=True) llm._run_server(use_tqdm=True)
end = time.time() end = time.time()
return end - start
def run_hf(
requests: List[Tuple[str, int, int]],
model: str,
tokenizer: PreTrainedTokenizerBase,
n: int,
use_beam_search: bool,
max_batch_size: int,
) -> float:
assert not use_beam_search
tokenizer = get_tokenizer(model)
llm = AutoModelForCausalLM.from_pretrained(
model, torch_dtype=torch.float16)
llm = llm.cuda()
pbar = tqdm(total=len(requests))
start = time.time()
batch: List[str] = []
max_prompt_len = 0
max_output_len = 0
for i in range(len(requests)):
prompt, prompt_len, output_len = requests[i]
# Add the prompt to the batch.
batch.append(prompt)
max_prompt_len = max(max_prompt_len, prompt_len)
max_output_len = max(max_output_len, output_len)
if len(batch) < max_batch_size and i != len(requests) - 1:
# Check if we can add more requests to the batch.
_, next_prompt_len, next_output_len = requests[i + 1]
if (max(max_prompt_len, next_prompt_len) + max(
max_output_len, next_output_len)) <= 2048:
# We can add more requests to the batch.
continue
# Generate the sequences.
input_ids = tokenizer(batch, return_tensors="pt", padding=True).input_ids
llm_outputs = llm.generate(
input_ids=input_ids.cuda(),
do_sample=not use_beam_search,
num_return_sequences=n,
temperature=1.0,
top_p=1.0,
use_cache=True,
max_new_tokens=max_output_len,
)
# Include the decoding time.
tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
pbar.update(len(batch))
# Clear the batch.
batch = []
max_prompt_len = 0
max_output_len = 0
end = time.time()
return end - start
def main(args: argparse.Namespace):
print(args)
random.seed(args.seed)
# Sample the requests.
tokenizer = get_tokenizer(args.model)
requests = sample_requests(args.dataset, args.num_prompts, tokenizer)
if args.backend == "cacheflow":
elapsed_time = run_cacheflow(
requests, args.model, args.tensor_parallel_size, args.seed, args.n,
args.use_beam_search)
elif args.backend == "hf":
assert args.tensor_parallel_size == 1
elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
args.use_beam_search, args.hf_max_batch_size)
else:
raise ValueError(f"Unknown backend: {args.backend}")
total_num_tokens = sum( total_num_tokens = sum(
len(prompt_token_ids) + output_len prompt_len + output_len
for prompt_token_ids, output_len in requests for _, prompt_len, output_len in requests
) )
elapsed_time = end - start
print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, " print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
f"{total_num_tokens / elapsed_time:.2f} tokens/s") f"{total_num_tokens / elapsed_time:.2f} tokens/s")
if __name__ == "__main__": if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Benchmark the throughput.") parser = argparse.ArgumentParser(description="Benchmark the throughput.")
parser.add_argument("--backend", type=str, choices=["cacheflow", "hf"],
default="cacheflow")
parser.add_argument("--dataset", type=str, required=True, parser.add_argument("--dataset", type=str, required=True,
help="Path to the dataset.") help="Path to the dataset.")
parser.add_argument("--model", type=str, default="facebook/opt-125m") parser.add_argument("--model", type=str, default="facebook/opt-125m")
...@@ -102,5 +203,14 @@ if __name__ == "__main__": ...@@ -102,5 +203,14 @@ if __name__ == "__main__":
parser.add_argument("--num-prompts", type=int, default=1000, parser.add_argument("--num-prompts", type=int, default=1000,
help="Number of prompts to process.") help="Number of prompts to process.")
parser.add_argument("--seed", type=int, default=0) parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--hf-max-batch-size", type=int, default=None,
help="Maximum batch size for HF backend.")
args = parser.parse_args() args = parser.parse_args()
if args.backend == "cacheflow":
if args.hf_max_batch_size is not None:
raise ValueError("HF max batch size is only for HF backend.")
elif args.backend == "hf":
if args.hf_max_batch_size is None:
raise ValueError("HF max batch size is required for HF backend.")
main(args) main(args)
benchmarks/launch_tgi_server.sh 0 → 100755
View file @ 311490a7
#!/bin/bash
PORT=8001
MODEL=$1
TOKENS=$2
docker run --gpus all --shm-size 1g -p $PORT:80 \
-v $PWD/data:/data \
ghcr.io/huggingface/text-generation-inference:0.8 \
--model-id $MODEL \
--sharded false \
--max-input-length 1024 \
--max-total-tokens 2048 \
--max-best-of 5 \
--max-concurrent-requests 5000 \
--max-batch-total-tokens $TOKENS
benchmarks/trace.py deleted 100644 → 0
View file @ da5ddcd5
import pickle
import random
from typing import List, Tuple
import numpy as np
from cacheflow.sampling_params import SamplingParams
def generate_text_completion_requests(
dataset: str,
request_rate: float,
duration: int,
seed: int,
n1: float = 0.0,
n2: float = 0.0,
n3: float = 0.0,
n4: float = 0.0,
n6: float = 0.0,
n2_beam: float = 0.0,
n4_beam: float = 0.0,
n6_beam: float = 0.0,
n8_beam: float = 0.0,
max_seq_len: int = 2048,
time_quantum: int = 10,
) -> List[Tuple[float, List[int], SamplingParams]]:
random.seed(seed)
np.random.seed(seed)
# Generate timestamps for requests using Poisson distribution.
lam = request_rate * (time_quantum / 1000)
quantums_per_sec = 1000 / time_quantum
arrival_times = np.random.poisson(
lam=lam, size=int(duration * quantums_per_sec))
timestamps = []
for i, n in enumerate(arrival_times):
timestamps += [i * (time_quantum / 1000)] * n
# Load and shuffle the dataset.
num_requests = len(timestamps)
with open(dataset, 'rb') as f:
data = pickle.load(f)
filtered = []
for pair in data:
input_tokens, output_tokens = pair
input_len = len(input_tokens)
output_len = len(output_tokens)
# Filter out too long sequences.
if input_len + output_len < max_seq_len:
# Output tokens are not needed for the benchmark.
filtered.append((input_tokens, output_len))
data = []
while len(data) < num_requests:
data += filtered
data = data[:num_requests]
# Shuffle the data.
assert len(data) == len(timestamps)
random.shuffle(data)
random_sampling_params_dict = {
'temperature': 1.0,
'top_p': 1.0,
'use_beam_search': False,
'stop_token_ids': set(),
'num_logprobs': 0,
'context_window_size': None,
}
beam_search_params_dict = {
'temperature': 0.0,
'top_p': 1.0,
'use_beam_search': True,
'stop_token_ids': set(),
'num_logprobs': 0,
'context_window_size': None,
}
# Generate requests based on the sampling parameter ratio.
requests = []
assert n1 + n2 + n3 + n4 + n6 + n2_beam + n4_beam + n6_beam + n8_beam == 1.0
cum_sum = 0
for timestamp, pair in zip(timestamps, data):
input_tokens, output_len = pair
if cum_sum < n1 * num_requests:
sampling_params = SamplingParams(
n=1, max_num_steps=output_len, **random_sampling_params_dict)
elif cum_sum < (n1 + n2) * num_requests:
sampling_params = SamplingParams(
n=2, max_num_steps=output_len, **random_sampling_params_dict)
elif cum_sum < (n1 + n2 + n3) * num_requests:
sampling_params = SamplingParams(
n=3, max_num_steps=output_len, **random_sampling_params_dict)
elif cum_sum < (n1 + n2 + n3 + n4) * num_requests:
sampling_params = SamplingParams(
n=4, max_num_steps=output_len, **random_sampling_params_dict)
elif cum_sum < (n1 + n2 + n3 + n4 + n6) * num_requests:
sampling_params = SamplingParams(
n=6, max_num_steps=output_len, **random_sampling_params_dict)
elif cum_sum < (n1 + n2 + n3 + n4 + n6 + n2_beam) * num_requests:
sampling_params = SamplingParams(
n=2, max_num_steps=output_len, **beam_search_params_dict)
elif cum_sum < (n1 + n2 + n3 + n4 + n6 + n2_beam + n4_beam) * num_requests:
sampling_params = SamplingParams(
n=4, max_num_steps=output_len, **beam_search_params_dict)
elif cum_sum < (n1 + n2 + n3 + n4 + n6 + n2_beam + n4_beam + n6_beam) * num_requests:
sampling_params = SamplingParams(
n=6, max_num_steps=output_len, **beam_search_params_dict)
elif cum_sum < (n1 + n2 + n3 + n4 + n6 + n2_beam + n4_beam + n6_beam + n8_beam) * num_requests:
sampling_params = SamplingParams(
n=8, max_num_steps=output_len, **beam_search_params_dict)
else:
raise ValueError('Invalid request ratio.')
cum_sum += 1
requests.append((timestamp, input_tokens, sampling_params))
return requests
cacheflow/server/arg_utils.py
View file @ 311490a7
...@@ -120,6 +120,7 @@ class ServerArgs: ...@@ -120,6 +120,7 @@ class ServerArgs:
class AsyncServerArgs(ServerArgs): class AsyncServerArgs(ServerArgs):
"""Arguments for asynchronous CacheFlow servers.""" """Arguments for asynchronous CacheFlow servers."""
server_use_ray: bool = False server_use_ray: bool = False
disable_log_requests: bool = False
@staticmethod @staticmethod
def add_cli_args( def add_cli_args(
...@@ -129,4 +130,6 @@ class AsyncServerArgs(ServerArgs): ...@@ -129,4 +130,6 @@ class AsyncServerArgs(ServerArgs):
parser.add_argument('--server-use-ray', action='store_true', parser.add_argument('--server-use-ray', action='store_true',
help='use Ray to start the LLM server in a ' help='use Ray to start the LLM server in a '
'separate process as the web server process.') 'separate process as the web server process.')
parser.add_argument('--disable-log-requests', action='store_true',
help='disable logging requests')
return parser return parser
cacheflow/server/async_llm_server.py
View file @ 311490a7
...@@ -32,12 +32,14 @@ class AsyncLLMServer: ...@@ -32,12 +32,14 @@ class AsyncLLMServer:
server_use_ray: Whether to make LLMServer a Ray actor. If so, the server_use_ray: Whether to make LLMServer a Ray actor. If so, the
async frontend will be executed in a separate process as the async frontend will be executed in a separate process as the
model workers. model workers.
log_requests: Whether to log the requests.
*args, *kwargs: Arguments for LLMServer. *args, *kwargs: Arguments for LLMServer.
""" """
def __init__(self, worker_use_ray: bool, server_use_ray: bool, def __init__(self, worker_use_ray: bool, server_use_ray: bool,
*args, **kwargs) -> None: log_requests: bool = True, *args, **kwargs) -> None:
self.worker_use_ray = worker_use_ray self.worker_use_ray = worker_use_ray
self.server_use_ray = server_use_ray self.server_use_ray = server_use_ray
self.log_requests = log_requests
if not self.server_use_ray: if not self.server_use_ray:
server_class = LLMServer server_class = LLMServer
elif self.worker_use_ray: elif self.worker_use_ray:
...@@ -106,6 +108,7 @@ class AsyncLLMServer: ...@@ -106,6 +108,7 @@ class AsyncLLMServer:
request_event = asyncio.Event() request_event = asyncio.Event()
self.request_events[request_id] = request_event self.request_events[request_id] = request_event
if self.log_requests:
logger.info(f"Received request {request_id}: " logger.info(f"Received request {request_id}: "
f"prompt: {prompt!r}, " f"prompt: {prompt!r}, "
f"sampling params: {sampling_params}, " f"sampling params: {sampling_params}, "
...@@ -152,6 +155,7 @@ class AsyncLLMServer: ...@@ -152,6 +155,7 @@ class AsyncLLMServer:
# Once finished, release the resources of the sequence group. # Once finished, release the resources of the sequence group.
if request_output.finished(): if request_output.finished():
if self.log_requests:
logger.info(f"Finished request {request_id}.") logger.info(f"Finished request {request_id}.")
del self.request_outputs[request_id] del self.request_outputs[request_id]
...@@ -176,6 +180,7 @@ class AsyncLLMServer: ...@@ -176,6 +180,7 @@ class AsyncLLMServer:
# The request has already finished or been aborted. # The request has already finished or been aborted.
return return
if self.log_requests:
logger.info(f"Aborted request {request_id}.") logger.info(f"Aborted request {request_id}.")
if self.server_use_ray: if self.server_use_ray:
...@@ -206,6 +211,7 @@ class AsyncLLMServer: ...@@ -206,6 +211,7 @@ class AsyncLLMServer:
# Create the LLM server. # Create the LLM server.
server = cls(server_args.worker_use_ray, server = cls(server_args.worker_use_ray,
server_args.server_use_ray, server_args.server_use_ray,
not server_args.disable_log_requests,
*server_configs, *server_configs,
distributed_init_method, devices, distributed_init_method, devices,
log_stats=not server_args.disable_log_stats) log_stats=not server_args.disable_log_stats)
......
examples/simple_fastapi_client.py
View file @ 311490a7
import argparse import argparse
import json import json
import requests
from typing import Iterable, List from typing import Iterable, List
def clear_line(n:int = 1) -> None: import requests
def clear_line(n: int = 1) -> None:
LINE_UP = '\033[1A' LINE_UP = '\033[1A'
LINE_CLEAR = '\x1b[2K' LINE_CLEAR = '\x1b[2K'
for i in range(n): for _ in range(n):
print(LINE_UP, end=LINE_CLEAR, flush=True) print(LINE_UP, end=LINE_CLEAR, flush=True)
...@@ -53,7 +55,7 @@ if __name__ == "__main__": ...@@ -53,7 +55,7 @@ if __name__ == "__main__":
n = args.n n = args.n
stream = args.stream stream = args.stream
print(f"Prompt: {prompt}\n", flush=True) print(f"Prompt: {prompt!r}\n", flush=True)
response = post_http_request(prompt, api_url, n, stream) response = post_http_request(prompt, api_url, n, stream)
if stream: if stream:
...@@ -63,8 +65,8 @@ if __name__ == "__main__": ...@@ -63,8 +65,8 @@ if __name__ == "__main__":
num_printed_lines = 0 num_printed_lines = 0
for i, line in enumerate(h): for i, line in enumerate(h):
num_printed_lines += 1 num_printed_lines += 1
print(f"Beam candidate {i}: {line}", flush=True) print(f"Beam candidate {i}: {line!r}", flush=True)
else: else:
output = get_response(response) output = get_response(response)
for i, line in enumerate(output): for i, line in enumerate(output):
print(f"Beam candidate {i}: {line}", flush=True) print(f"Beam candidate {i}: {line!r}", flush=True)
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