Clean up the server script

This commit is contained in:
Woosuk Kwon 2023-02-24 11:56:21 +00:00
parent 6aef2278f4
commit fa16389a2e

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@ -1,6 +1,7 @@
import argparse
from typing import List
from cacheflow.master.frontend import Frontend
from cacheflow.master.scheduler import Scheduler
from cacheflow.worker.controller import Controller
@ -8,15 +9,15 @@ parser = argparse.ArgumentParser(description='CacheFlow server')
parser.add_argument('--model', type=str, default='facebook/opt-125m', help='model name')
parser.add_argument('--num-nodes', type=int, default=1, help='number of nodes')
parser.add_argument('--num-workers', type=int, default=1, help='number of workers per node')
parser.add_argument('--block-size', type=int, default=8, help='block size')
parser.add_argument('--num-gpu-blocks', type=int, default=1024, help='number of GPU blocks')
parser.add_argument('--num-cpu-blocks', type=int, default=256, help='number of CPU blocks')
parser.add_argument('--block-size', type=int, default=8, help='token block size')
# TODO(woosuk): Add an analytical model to determine the maximum number of GPU/CPU blocks.
parser.add_argument('--num-gpu-blocks', type=int, default=1024, help='number of GPU blocks (per GPU)')
parser.add_argument('--num-cpu-blocks', type=int, default=256, help='number of CPU blocks (per GPU)')
args = parser.parse_args()
def main():
args = parser.parse_args()
# Create controllers.
# Create a controller for each node.
controllers: List[Controller] = []
for i in range(args.num_nodes):
controller = Controller(
@ -26,12 +27,18 @@ def main():
block_size=args.block_size,
num_gpu_blocks=args.num_gpu_blocks,
num_cpu_blocks=args.num_cpu_blocks,
dtype='float',
)
controllers.append(controller)
# Create a frontend.
frontend = Frontend(
model_name=args.model,
block_size=args.block_size,
)
# Create a scheduler.
scheduler = Scheduler(
frontend=frontend,
controllers=controllers,
block_size=args.block_size,
num_gpu_blocks=args.num_gpu_blocks,
@ -42,65 +49,19 @@ def main():
controllers[i].set_next(controllers[i + 1])
controllers[-1].set_next(scheduler)
# seq_groups, max_num_steps, stop_token_ids = generate_inputs(1000, args.block_size)
seq_groups, max_num_steps, stop_token_ids = test_inputs(args.block_size)
scheduler.pending.extend(seq_groups)
scheduler.max_num_steps.update(max_num_steps)
scheduler.stop_token_ids.update(stop_token_ids)
test_inputs = [
'Ion Stoica is a',
'UC Berkeley is',
'The future of cloud computing is',
]
for prompt in test_inputs:
frontend.query(prompt)
while scheduler.pending or scheduler.running:
scheduler.prepare()
# FIXME
while True:
scheduler.step()
def test_inputs(block_size):
from cacheflow.sequence import Sequence
from cacheflow.sequence import SequenceGroup
from cacheflow.utils import Counter
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('facebook/opt-125m')
prompt = "Hello, I'm am conscious and"
prompt_tokens = tokenizer.encode(prompt)
seq = Sequence(0, prompt_tokens, block_size=block_size)
seq_group = SequenceGroup(0, [seq])
seq_groups = [seq_group]
max_num_steps = {0: 8}
stop_token_ids = {0: []}
return seq_groups, max_num_steps, stop_token_ids
def generate_inputs(num_inputs, block_size):
import random
random.seed(0)
from cacheflow.sequence import Sequence
from cacheflow.sequence import SequenceGroup
from cacheflow.utils import Counter
seq_group_counter = Counter()
seq_counter = Counter()
max_num_steps = {}
stop_token_ids = {}
seq_groups = []
for _ in range(num_inputs):
seq_group_id = next(seq_group_counter)
prompt_len = random.randint(16, 128)
max_num_steps[seq_group_id] = random.randint(32, 1024)
stop_token_ids[seq_group_id] = []
seqs = []
for _ in range(2):
seq_id = next(seq_counter)
seq = Sequence(seq_id, [0] * prompt_len, block_size=block_size)
seqs.append(seq)
seq_group = SequenceGroup(seq_group_id, seqs)
seq_groups.append(seq_group)
return seq_groups, max_num_steps, stop_token_ids
if not scheduler.pending and not scheduler.running:
break
if __name__ == '__main__':