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import logging
from abc import ABC, abstractmethod, abstractproperty
from typing import Any, List, Optional, Set, Type, Union
import torch
from vllm.lora.models import (TARGET_MODULES_QKV, LoRAModel, LoRAModelManager,
LRUCacheLoRAModelManager, create_lora_manager)
from vllm.lora.request import LoRARequest
from vllm.lora.layers import LoRAMapping
from vllm.config import LoRAConfig
logger = logging.getLogger(__name__)
class WorkerLoRAManager(ABC):
"""Abstract class for managing LoRA models on the worker side."""
def __init__(self, max_num_seqs: int, max_num_batched_tokens: int,
vocab_size: int, lora_config: LoRAConfig,
device: torch.device):
self.max_num_seqs = max_num_seqs
self.max_num_batched_tokens = max_num_batched_tokens
self.vocab_size = vocab_size
self.device = device
self.lora_config = lora_config
@abstractproperty
def is_enabled(self) -> bool:
...
@abstractmethod
def create_lora_manager(
self,
model: torch.nn.Module,
target_modules: Union[str, List[str]] = TARGET_MODULES_QKV,
) -> Any:
...
@abstractmethod
def set_active_loras(self, lora_requests: List[LoRARequest],
lora_mapping: LoRAMapping) -> None:
...
@abstractmethod
def add_lora(self, lora_request: LoRARequest) -> bool:
...
@abstractmethod
def add_dummy_lora(self, lora_request: LoRARequest, rank: int) -> bool:
...
@abstractmethod
def remove_lora(self, lora_id: int) -> bool:
...
@abstractmethod
def remove_all_loras(self) -> bool:
...
@abstractmethod
def list_loras(self) -> Set[int]:
...
class WorkerLoRAManager(WorkerLoRAManager):
"""WorkerLoRAManager that manages LoRA models on the worker side.
Every request, the requested LoRAs will be loaded (unless they are already
loaded), and every other LoRA will be unloaded."""
_lora_manager_cls: Type[LoRAModelManager] = LoRAModelManager
def __init__(
self,
max_num_seqs: int,
max_num_batched_tokens: int,
vocab_size: int,
lora_config: LoRAConfig,
device: torch.device,
lora_model_cls: Type[LoRAModel] = LoRAModel,
):
self._lora_manager: Optional[LoRAModelManager] = None
self._lora_model_cls = lora_model_cls
super().__init__(max_num_seqs, max_num_batched_tokens, vocab_size,
lora_config, device)
@property
def is_enabled(self) -> bool:
return True
def create_lora_manager(
self,
model: torch.nn.Module,
target_modules: Union[str, List[str]] = TARGET_MODULES_QKV,
) -> Any:
lora_manager = create_lora_manager(
model,
max_num_seqs=self.max_num_seqs,
max_num_batched_tokens=self.max_num_batched_tokens,
target_modules=target_modules,
vocab_size=self.vocab_size,
lora_config=self.lora_config,
lora_manager_cls=self._lora_manager_cls,
)
self._lora_manager: LoRAModelManager = lora_manager
return lora_manager.model
def set_active_loras(self, lora_requests: List[LoRARequest],
lora_mapping: LoRAMapping) -> None:
self._apply_loras(lora_requests)
self._lora_manager.set_lora_mapping(lora_mapping)
def _apply_loras(self, lora_requests: List[LoRARequest]) -> None:
loras_that_exist = self.list_loras()
loras_map = {
lora_request.lora_int_id: lora_request
for lora_request in lora_requests if lora_request
}
if len(loras_map) > self._lora_manager.lora_slots:
raise RuntimeError(
f"Number of requested LoRAs ({len(loras_map)}) is greater "
"than the number of GPU LoRA slots "
f"({self._lora_manager.lora_slots}).")
new_loras = set(loras_map)
loras_to_add = new_loras - loras_that_exist
loras_to_remove = loras_that_exist - new_loras
for lora_id in loras_to_remove:
self.remove_lora(lora_id)
for lora_id in loras_to_add:
self.add_lora(loras_map[lora_id])
def _load_lora(self, lora_request: LoRARequest) -> LoRAModel:
try:
lora = self._lora_model_cls.from_local_checkpoint(
lora_request.lora_local_path,
lora_model_id=lora_request.lora_int_id,
device="cpu",
dtype=self.lora_config.lora_dtype,
target_embedding_padding=self.vocab_size +
self.lora_config.lora_extra_vocab_size,
)
except Exception as e:
raise RuntimeError(
f"Loading lora {lora_request.lora_local_path} failed") from e
if lora.rank > self.lora_config.max_lora_rank:
raise ValueError(
f"LoRA rank {lora.rank} is greater than max_lora_rank "
f"{self.lora_config.max_lora_rank}.")
if lora.extra_vocab_size > self.lora_config.lora_extra_vocab_size:
raise ValueError(
f"LoRA added vocab size {lora.extra_vocab_size} is greater than "
f"lora_extra_vocab_size {self.lora_config.lora_extra_vocab_size}."
)
return lora
def add_dummy_lora(self, lora_request: LoRARequest, rank: int) -> bool:
if lora_request.lora_int_id in self.list_loras():
return False
return self._lora_manager.add_lora(
self._lora_manager.create_dummy_lora(lora_request.lora_int_id,
rank))
def add_lora(self, lora_request: LoRARequest) -> bool:
if lora_request.lora_int_id in self.list_loras():
return False
lora = self._load_lora(lora_request)
loaded = self._lora_manager.add_lora(lora)
self._lora_manager.activate_lora(lora.id)
return loaded
def remove_lora(self, lora_id: int) -> bool:
return self._lora_manager.remove_lora(lora_id)
def remove_all_loras(self) -> bool:
self._lora_manager.remove_all_loras()
def list_loras(self) -> Set[int]:
return set(self._lora_manager.list_loras())
class LRUCacheWorkerLoRAManager(WorkerLoRAManager):
"""WorkerLoRAManager that manages LoRA models on the worker side.
Uses an LRU Cache. Every request, the requested LoRAs will be loaded
(unless they are already loaded) and least recently used LoRAs will
be unloaded if the cache is above capacity."""
_lora_manager_cls: Type[
LRUCacheLoRAModelManager] = LRUCacheLoRAModelManager
def create_lora_manager(
self,
model: torch.nn.Module,
target_modules: Union[str, List[str]] = TARGET_MODULES_QKV,
) -> Any:
lora_manager = create_lora_manager(
model,
target_modules=target_modules,
lora_manager_cls=self._lora_manager_cls,
max_num_seqs=self.max_num_seqs,
vocab_size=self.vocab_size,
lora_config=self.lora_config,
max_num_batched_tokens=self.max_num_batched_tokens,
)
self._lora_manager: LRUCacheLoRAModelManager = lora_manager
return lora_manager.model
def _apply_loras(self, lora_requests: List[LoRARequest]) -> None:
loras_map = {
lora_request.lora_int_id: lora_request
for lora_request in lora_requests if lora_request
}
if len(loras_map) > self._lora_manager.lora_slots:
raise RuntimeError(
f"Number of requested LoRAs ({len(loras_map)}) is greater "
"than the number of GPU LoRA slots "
f"({self._lora_manager.lora_slots}).")
for lora in loras_map.values():
self.add_lora(lora)
def add_lora(self, lora_request: LoRARequest) -> bool:
if lora_request.lora_int_id not in self.list_loras():
# Remove before we load the new lora to save memory
if len(self._lora_manager) + 1 > self._lora_manager.capacity:
self._lora_manager.remove_oldest_lora()
lora = self._load_lora(lora_request)
loaded = self._lora_manager.add_lora(lora)
else:
# If the lora is already loaded, just touch it to
# update its position in the caches
loaded = self._lora_manager.get_lora(lora_request.lora_int_id)
self._lora_manager.activate_lora(lora_request.lora_int_id)
return loaded
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