182 lines
7.0 KiB
Python
182 lines
7.0 KiB
Python
import argparse
|
|
from typing import List, Tuple
|
|
import random
|
|
|
|
import ray
|
|
|
|
from cacheflow.master.scheduler import Scheduler
|
|
from cacheflow.models import get_memory_analyzer
|
|
from cacheflow.worker.controller import Controller, DeviceID
|
|
from cacheflow.sequence import SequenceGroup
|
|
from cacheflow.sampling_params import SamplingParams
|
|
|
|
|
|
class Server:
|
|
def __init__(
|
|
self,
|
|
model: str,
|
|
model_path: str,
|
|
pipeline_parallel_size: int,
|
|
tensor_parallel_size: int,
|
|
block_size: int,
|
|
dtype: str,
|
|
seed: int,
|
|
swap_space: int,
|
|
max_batch_size: int,
|
|
num_nodes: int,
|
|
num_devices_per_node: int,
|
|
distributed_init_method: str,
|
|
all_stage_devices: List[List[DeviceID]],
|
|
gpu_memory: int,
|
|
cpu_memory: int,
|
|
):
|
|
self.num_nodes = num_nodes
|
|
self.num_devices_per_node = num_devices_per_node
|
|
self.world_size = pipeline_parallel_size * tensor_parallel_size
|
|
|
|
self.memory_analyzer = get_memory_analyzer(
|
|
model_name=model,
|
|
block_size=block_size,
|
|
dtype=dtype,
|
|
gpu_memory=gpu_memory,
|
|
cpu_memory=cpu_memory,
|
|
tensor_parallel_size=tensor_parallel_size,
|
|
)
|
|
self.num_gpu_blocks = self.memory_analyzer.get_max_num_gpu_blocks(
|
|
max_num_batched_tokens=max_batch_size)
|
|
self.num_cpu_blocks = self.memory_analyzer.get_max_num_cpu_blocks(
|
|
swap_space=swap_space)
|
|
print(f'# GPU blocks: {self.num_gpu_blocks}, '
|
|
f'# CPU blocks: {self.num_cpu_blocks}')
|
|
|
|
# Create a controller for each pipeline stage.
|
|
self.controllers: List[Controller] = []
|
|
for i in range(pipeline_parallel_size):
|
|
controller = Controller(
|
|
stage_id=i,
|
|
stage_devices=all_stage_devices[i],
|
|
world_size=self.world_size,
|
|
pipeline_parallel_size=pipeline_parallel_size,
|
|
tensor_parallel_size=tensor_parallel_size,
|
|
distributed_init_method=distributed_init_method,
|
|
model_name=model,
|
|
block_size=block_size,
|
|
num_gpu_blocks=self.num_gpu_blocks,
|
|
num_cpu_blocks=self.num_cpu_blocks,
|
|
dtype=dtype,
|
|
seed=seed,
|
|
model_path=model_path,
|
|
)
|
|
self.controllers.append(controller)
|
|
|
|
# Create a scheduler.
|
|
self.scheduler = Scheduler(
|
|
controllers=self.controllers,
|
|
block_size=block_size,
|
|
num_gpu_blocks=self.num_gpu_blocks,
|
|
num_cpu_blocks=self.num_cpu_blocks,
|
|
max_num_batched_tokens=max_batch_size,
|
|
)
|
|
# Connect the controllers.
|
|
for i in range(len(self.controllers) - 1):
|
|
self.controllers[i].set_next(self.controllers[i + 1])
|
|
self.controllers[-1].set_next(self.scheduler)
|
|
|
|
def add_sequence_groups(
|
|
self,
|
|
sequence_groups: List[Tuple[SequenceGroup, SamplingParams]]
|
|
):
|
|
self.scheduler.add_sequence_groups(sequence_groups)
|
|
|
|
def step(self):
|
|
return self.scheduler.step()
|
|
|
|
def has_unfinished_requests(self):
|
|
return (self.scheduler.waiting or self.scheduler.running or
|
|
self.scheduler.swapped)
|
|
|
|
|
|
def initialize_ray_cluster(
|
|
address: str = 'auto',
|
|
pipeline_parallel_size: int = 1,
|
|
tensor_parallel_size: int = 1,
|
|
) -> Tuple[int, int, str, List[List[DeviceID]]]:
|
|
# Connect to a ray cluster.
|
|
ray.init(address=address)
|
|
|
|
# Assume we have a uniform cluster that each node has the same number of
|
|
# GPUs for now.
|
|
valid_node_resources = []
|
|
num_devices_per_node = None
|
|
for node in ray.nodes():
|
|
if (not node['Alive']) or node['Resources']['GPU'] <= 0:
|
|
continue
|
|
if num_devices_per_node is None:
|
|
num_devices_per_node = node['Resources']['GPU']
|
|
else:
|
|
assert num_devices_per_node == node['Resources']['GPU'], (
|
|
"The number of GPUs per node is not uniform.")
|
|
for key in node['Resources']:
|
|
if key.startswith('node:'):
|
|
valid_node_resources.append(key)
|
|
|
|
num_nodes = len(valid_node_resources)
|
|
|
|
assert (pipeline_parallel_size * tensor_parallel_size
|
|
<= num_nodes * num_devices_per_node), (
|
|
"The number of required GPUs exceeds the total number of "
|
|
"available GPUs.")
|
|
if tensor_parallel_size >= num_devices_per_node:
|
|
assert tensor_parallel_size % num_devices_per_node == 0, (
|
|
"The number of tensor parallelism is not divisible by the "
|
|
"number of GPUs per node.")
|
|
else:
|
|
assert num_devices_per_node % tensor_parallel_size == 0, (
|
|
"The number of GPUs per node is not divisible by the number "
|
|
"of tensor parallelism.")
|
|
|
|
# Assign GPUs to pipeline stages.
|
|
rank = 0
|
|
current_node_id = 0
|
|
current_device_id = 0
|
|
distributed_init_method = None
|
|
all_stage_devices = []
|
|
|
|
for i in range(pipeline_parallel_size):
|
|
stage_devices = []
|
|
for j in range(tensor_parallel_size):
|
|
node_resource = valid_node_resources[current_node_id]
|
|
stage_devices.append((rank, node_resource, current_device_id))
|
|
if distributed_init_method is None:
|
|
ip = node_resource.split("node:")[-1]
|
|
port = random.randint(10000, 20000)
|
|
distributed_init_method = f"tcp://{ip}:{port}"
|
|
rank += 1
|
|
current_device_id += 1
|
|
if current_device_id >= num_devices_per_node:
|
|
current_node_id += 1
|
|
current_device_id = 0
|
|
all_stage_devices.append(stage_devices)
|
|
|
|
return (num_nodes, num_devices_per_node, distributed_init_method,
|
|
all_stage_devices)
|
|
|
|
|
|
def add_server_arguments(parser: argparse.ArgumentParser):
|
|
# Model arguments
|
|
parser.add_argument('--model', type=str, default='facebook/opt-125m', help='model name')
|
|
parser.add_argument('--model-path', type=str, default='~/.cacheflow/model_weights',
|
|
help='model path to download and load the weights')
|
|
# Parallel arguments
|
|
parser.add_argument('--pipeline-parallel-size', type=int, default=1, help='number of pipeline stages')
|
|
parser.add_argument('--tensor-parallel-size', type=int, default=1, help='number of tensor parallel replicas')
|
|
# KV cache arguments
|
|
parser.add_argument('--block-size', type=int, default=8, choices=[8, 16], help='token block size')
|
|
# NOTE(woosuk): If FlashAttention is used, the float data type is not supported.
|
|
parser.add_argument('--dtype', type=str, default='half', choices=['half', 'float'], help='data type')
|
|
# TODO(woosuk): Support fine-grained seeds (e.g., seed per request).
|
|
parser.add_argument('--seed', type=int, default=0, help='random seed')
|
|
parser.add_argument('--swap-space', type=int, default=20, help='CPU swap space size (GiB) per GPU')
|
|
parser.add_argument('--max-batch-size', type=int, default=2560, help='maximum number of batched tokens')
|
|
return parser
|