mirror of
https://github.com/vllm-project/vllm.git
synced 2025-10-31 22:44:37 +08:00
297 lines
12 KiB
Python
297 lines
12 KiB
Python
import argparse
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from typing import List, Tuple, Optional
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import random
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import torch
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try:
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import ray
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except ImportError:
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ray = None
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from cacheflow.core.scheduler import Scheduler
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from cacheflow.frontend.simple_frontend import SimpleFrontend
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from cacheflow.logger import init_logger
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from cacheflow.model_executor import get_memory_analyzer
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from cacheflow.sampling_params import SamplingParams
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from cacheflow.sequence import SequenceGroup
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from cacheflow.utils import get_gpu_memory, get_cpu_memory
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from cacheflow.worker.controller import Controller, DeviceID
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logger = init_logger(__name__)
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class Server:
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def __init__(
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self,
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model: str,
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cache_dir: Optional[str],
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use_dummy_weights: bool,
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use_np_cache: bool,
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pipeline_parallel_size: int,
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tensor_parallel_size: int,
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block_size: int,
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dtype: str,
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seed: int,
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swap_space: int,
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max_num_batched_tokens: int,
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max_num_sequences: int,
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num_nodes: int,
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num_devices_per_node: int,
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distributed_init_method: str,
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all_stage_devices: List[List[DeviceID]],
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gpu_memory: int,
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cpu_memory: int,
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use_ray: bool,
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collect_stats: bool = False,
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do_memory_analysis: bool = False,
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):
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logger.info(
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"Initializing a server with config: "
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f"model={model!r}, "
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f"dtype={dtype}, "
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f"use_dummy_weights={use_dummy_weights}, "
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f"cache_dir={cache_dir}, "
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f"use_np_cache={use_np_cache}, "
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f"tensor_parallel_size={tensor_parallel_size}, "
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f"block_size={block_size}, "
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f"seed={seed})"
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)
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self.num_nodes = num_nodes
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self.num_devices_per_node = num_devices_per_node
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self.world_size = pipeline_parallel_size * tensor_parallel_size
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if not use_ray:
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assert self.world_size == 1, (
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"Only support single GPU without Ray.")
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self.memory_analyzer = get_memory_analyzer(
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model_name=model,
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block_size=block_size,
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dtype=dtype,
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gpu_memory=gpu_memory,
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cpu_memory=cpu_memory,
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tensor_parallel_size=tensor_parallel_size,
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)
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self.num_gpu_blocks = self.memory_analyzer.get_max_num_gpu_blocks(
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max_num_batched_tokens=max_num_batched_tokens)
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self.num_cpu_blocks = self.memory_analyzer.get_max_num_cpu_blocks(
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swap_space_gib=swap_space)
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# Create a controller for each pipeline stage.
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self.controllers: List[Controller] = []
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for i in range(pipeline_parallel_size):
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controller = Controller(
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stage_id=i,
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stage_devices=all_stage_devices[i],
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world_size=self.world_size,
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pipeline_parallel_size=pipeline_parallel_size,
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tensor_parallel_size=tensor_parallel_size,
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distributed_init_method=distributed_init_method,
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model_name=model,
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block_size=block_size,
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num_gpu_blocks=self.num_gpu_blocks,
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num_cpu_blocks=self.num_cpu_blocks,
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dtype=dtype,
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seed=seed,
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cache_dir=cache_dir,
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use_dummy_weights=use_dummy_weights,
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use_np_cache=use_np_cache,
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max_num_batched_tokens=max_num_batched_tokens,
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use_ray=use_ray,
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)
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self.controllers.append(controller)
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# Create a scheduler.
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self.scheduler = Scheduler(
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controllers=self.controllers,
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block_size=block_size,
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num_gpu_blocks=self.num_gpu_blocks,
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num_cpu_blocks=self.num_cpu_blocks,
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max_num_batched_tokens=max_num_batched_tokens,
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max_num_sequences=max_num_sequences,
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collect_stats=collect_stats,
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do_memory_analysis=do_memory_analysis,
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)
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# Connect the controllers.
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for i in range(len(self.controllers) - 1):
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self.controllers[i].set_next(self.controllers[i + 1])
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self.controllers[-1].set_next(self.scheduler)
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def add_sequence_groups(
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self,
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sequence_groups: List[Tuple[SequenceGroup, SamplingParams]]
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):
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self.scheduler.add_sequence_groups(sequence_groups)
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def step(self):
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return self.scheduler.step()
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def has_unfinished_requests(self):
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return (self.scheduler.waiting or self.scheduler.running or
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self.scheduler.swapped)
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def initialize_cluster(
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use_ray: bool = False,
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address: Optional[str] = None,
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pipeline_parallel_size: int = 1,
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tensor_parallel_size: int = 1,
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) -> Tuple[int, int, str, List[List[DeviceID]]]:
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# Initialize cluster locally.
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if not use_ray:
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assert pipeline_parallel_size * tensor_parallel_size == 1, (
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"Only support single GPU without Ray.")
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num_nodes = 1
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num_devices_per_node = torch.cuda.device_count()
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port = random.randint(10000, 20000)
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# We need to setup the distributed init method to make sure
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# the distributed megatron code (e.g., get world size) works correctly.
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distributed_init_method = f"tcp://localhost:{port}"
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all_stage_devices = [[(0, None, 0)]]
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return (num_nodes, num_devices_per_node, distributed_init_method,
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all_stage_devices)
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assert ray is not None, (
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"Ray is not installed. Please install Ray to use distributed "
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"serving.")
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# Connect to a ray cluster.
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ray.init(address=address)
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# Assume we have a uniform cluster that each node has the same number of
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# GPUs for now.
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valid_node_resources = []
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num_devices_per_node = None
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for node in ray.nodes():
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if (not node['Alive']) or node['Resources']['GPU'] <= 0:
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continue
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if num_devices_per_node is None:
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num_devices_per_node = node['Resources']['GPU']
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else:
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assert num_devices_per_node == node['Resources']['GPU'], (
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"The number of GPUs per node is not uniform.")
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for key in node['Resources']:
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if key.startswith('node:'):
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valid_node_resources.append(key)
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num_nodes = len(valid_node_resources)
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assert (pipeline_parallel_size * tensor_parallel_size
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<= num_nodes * num_devices_per_node), (
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"The number of required GPUs exceeds the total number of "
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"available GPUs.")
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if tensor_parallel_size >= num_devices_per_node:
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assert tensor_parallel_size % num_devices_per_node == 0, (
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"The number of tensor parallelism is not divisible by the "
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"number of GPUs per node.")
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else:
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assert num_devices_per_node % tensor_parallel_size == 0, (
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"The number of GPUs per node is not divisible by the number "
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"of tensor parallelism.")
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# Assign GPUs to pipeline stages.
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rank = 0
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current_node_id = 0
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current_device_id = 0
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distributed_init_method = None
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all_stage_devices = []
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for i in range(pipeline_parallel_size):
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stage_devices = []
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for j in range(tensor_parallel_size):
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node_resource = valid_node_resources[current_node_id]
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stage_devices.append((rank, node_resource, current_device_id))
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if distributed_init_method is None:
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ip = node_resource.split("node:")[-1]
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port = random.randint(10000, 20000)
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distributed_init_method = f"tcp://{ip}:{port}"
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rank += 1
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current_device_id += 1
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if current_device_id >= num_devices_per_node:
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current_node_id += 1
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current_device_id = 0
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all_stage_devices.append(stage_devices)
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return (num_nodes, num_devices_per_node, distributed_init_method,
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all_stage_devices)
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def add_server_arguments(parser: argparse.ArgumentParser):
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# Model arguments
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parser.add_argument('--model', type=str, default='facebook/opt-125m', help='model name')
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parser.add_argument('--cache-dir', type=str, default=None,
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help='cache dir to download and load the weights, '
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'default to the default cache dir of huggingface')
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parser.add_argument('--use-np-cache', action='store_true',
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help='save a numpy copy of model weights for faster loading')
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parser.add_argument('--use-dummy-weights', action='store_true', help='use dummy values for model weights')
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# TODO(woosuk): Support FP32 for debugging.
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parser.add_argument('--dtype', type=str, default='default', choices=['default', 'half', 'bfloat16'],
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help=('data type for model weights and activations. '
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'The "default" option will use FP16 precision '
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'for FP32 and FP16 models, and BF16 precision '
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'for BF16 models.'))
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# Parallel arguments
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parser.add_argument('--use-ray', action='store_true', help='use Ray for distributed serving, will be automatically set when using more than 1 GPU')
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parser.add_argument('--pipeline-parallel-size', '-pp', type=int, default=1, help='number of pipeline stages')
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parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1, help='number of tensor parallel replicas')
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# KV cache arguments
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parser.add_argument('--block-size', type=int, default=16, choices=[1, 2, 4, 8, 16, 32, 64, 128, 256], help='token block size')
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# TODO(woosuk): Support fine-grained seeds (e.g., seed per request).
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parser.add_argument('--seed', type=int, default=0, help='random seed')
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parser.add_argument('--swap-space', type=int, default=20, help='CPU swap space size (GiB) per GPU')
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parser.add_argument('--max-num-batched-tokens', type=int, default=2560, help='maximum number of batched tokens per iteration')
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parser.add_argument('--max-num-sequences', type=int, default=256, help='maximum number of sequences per iteration')
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return parser
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def process_server_arguments(args: argparse.Namespace):
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if args.pipeline_parallel_size * args.tensor_parallel_size > 1:
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args.use_ray = True
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return args
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def init_local_server_and_frontend_with_arguments(args: argparse.Namespace):
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# TODO(zhuohan): Support pipeline parallelism.
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assert args.pipeline_parallel_size == 1, (
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'Pipeline parallelism is not supported yet.')
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(num_nodes, num_devices_per_node, distributed_init_method,
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all_stage_devices) = (
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initialize_cluster(
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use_ray=args.use_ray,
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pipeline_parallel_size=args.pipeline_parallel_size,
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tensor_parallel_size=args.tensor_parallel_size))
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# Create a server.
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server = Server(
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model=args.model,
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cache_dir=args.cache_dir,
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use_dummy_weights=args.use_dummy_weights,
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use_np_cache=args.use_np_cache,
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pipeline_parallel_size=args.pipeline_parallel_size,
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tensor_parallel_size=args.tensor_parallel_size,
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block_size=args.block_size,
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dtype=args.dtype,
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seed=args.seed,
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swap_space=args.swap_space,
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max_num_batched_tokens=args.max_num_batched_tokens,
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max_num_sequences=args.max_num_sequences,
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num_nodes=num_nodes,
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num_devices_per_node=num_devices_per_node,
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distributed_init_method=distributed_init_method,
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all_stage_devices=all_stage_devices,
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gpu_memory=get_gpu_memory(),
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cpu_memory=get_cpu_memory(),
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use_ray=args.use_ray,
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)
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# Create a frontend.
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frontend = SimpleFrontend(
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model_name=args.model,
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block_size=args.block_size,
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)
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return server, frontend
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