vllm/vllm/prompt_adapter/worker_manager.py
Russell Bryant e489ad7a21
[Misc] Add SPDX-License-Identifier headers to python source files (#12628)
- **Add SPDX license headers to python source files**
- **Check for SPDX headers using pre-commit**

commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:18:24 2025 -0500

    Add SPDX license headers to python source files
    
This commit adds SPDX license headers to python source files as
recommended to
the project by the Linux Foundation. These headers provide a concise way
that is
both human and machine readable for communicating license information
for each
source file. It helps avoid any ambiguity about the license of the code
and can
    also be easily used by tools to help manage license compliance.
    
The Linux Foundation runs license scans against the codebase to help
ensure
    we are in compliance with the licenses of the code we use, including
dependencies. Having these headers in place helps that tool do its job.
    
    More information can be found on the SPDX site:
    
    - https://spdx.dev/learn/handling-license-info/
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea
Author: Russell Bryant <rbryant@redhat.com>
Date:   Fri Jan 31 14:36:32 2025 -0500

    Check for SPDX headers using pre-commit
    
    Signed-off-by: Russell Bryant <rbryant@redhat.com>

---------

Signed-off-by: Russell Bryant <rbryant@redhat.com>
2025-02-02 11:58:18 -08:00

179 lines
7.4 KiB
Python

# SPDX-License-Identifier: Apache-2.0
import logging
from typing import Any, Optional, Set, Type
import torch
from vllm.adapter_commons.utils import (add_adapter_worker,
apply_adapters_worker,
list_adapters_worker,
set_active_adapters_worker)
from vllm.adapter_commons.worker_manager import AbstractWorkerManager
from vllm.config import PromptAdapterConfig
from vllm.prompt_adapter.models import (LRUCachePromptAdapterModelManager,
PromptAdapterModel,
PromptAdapterModelManager,
create_prompt_adapter_manager)
from vllm.prompt_adapter.request import PromptAdapterRequest
logger = logging.getLogger(__name__)
class WorkerPromptAdapterManager(AbstractWorkerManager):
"""WorkerPromptAdapterManager that manages
prompt_adapter models on the worker side.
Every request, the requested prompt_adapters will be
loaded (unless they are already loaded),
and every other prompt_adapter will be unloaded."""
_manager_cls: Type[PromptAdapterModelManager] = PromptAdapterModelManager
def __init__(
self,
max_num_seqs: int,
max_num_batched_tokens: int,
device: torch.device,
prompt_adapter_config: PromptAdapterConfig,
prompt_adapter_model_cls: Type[PromptAdapterModel] = PromptAdapterModel
):
self._adapter_manager: PromptAdapterModelManager
self.max_num_seqs = max_num_seqs
self.max_num_batched_tokens = max_num_batched_tokens
self._prompt_adapter_model_cls = prompt_adapter_model_cls
self.prompt_adapter_config = prompt_adapter_config
super().__init__(device)
@property
def is_enabled(self) -> bool:
return True
def create_prompt_adapter_manager(
self,
model: torch.nn.Module,
) -> Any:
prompt_adapter_manager = create_prompt_adapter_manager(
model,
max_num_seqs=self.max_num_seqs,
max_num_batched_tokens=self.max_num_batched_tokens,
prompt_adapter_config=self.prompt_adapter_config,
prompt_adapter_manager_cls=self._manager_cls,
)
self._adapter_manager = prompt_adapter_manager
return prompt_adapter_manager.model
def _load_adapter(
self, prompt_adapter_request: PromptAdapterRequest
) -> PromptAdapterModel:
try:
prompt_adapter = (
self._prompt_adapter_model_cls.from_local_checkpoint(
prompt_adapter_request.prompt_adapter_local_path,
prompt_adapter_id=prompt_adapter_request.prompt_adapter_id,
num_virtual_tokens=prompt_adapter_request.
prompt_adapter_num_virtual_tokens,
config=self.prompt_adapter_config,
device=str(self.device),
))
except Exception as e:
raise RuntimeError(
f"Loading prompt_adapter "
f"{prompt_adapter_request.prompt_adapter_local_path}"
f" failed") from e
return prompt_adapter
def add_dummy_prompt_adapter(
self, prompt_adapter_request: PromptAdapterRequest) -> bool:
return True
def pin_adapter(self, adapter_id: int) -> bool:
return self._adapter_manager.pin_adapter(adapter_id)
def set_active_adapters(self, requests: Set[Any],
mapping: Optional[Any]) -> None:
set_active_adapters_worker(requests, mapping, self._apply_adapters,
self._adapter_manager.set_adapter_mapping)
def add_adapter(self, adapter_request: Any) -> bool:
return add_adapter_worker(adapter_request, self.list_adapters,
self._load_adapter,
self._adapter_manager.add_adapter,
self._adapter_manager.activate_adapter)
def _apply_adapters(self, adapter_requests: Set[Any]) -> None:
apply_adapters_worker(adapter_requests, self.list_adapters,
self._adapter_manager.adapter_slots,
self.remove_adapter, self.add_adapter)
def remove_adapter(self, adapter_id: int) -> bool:
return self._adapter_manager.remove_adapter(adapter_id)
def remove_all_adapters(self):
self._adapter_manager.remove_all_adapters()
def list_adapters(self) -> Set[int]:
return list_adapters_worker(self._adapter_manager.list_adapters)
class LRUCacheWorkerPromptAdapterManager(WorkerPromptAdapterManager):
"""WorkerPromptAdapterManager that manages
prompt_adapter models on the worker side.
Uses an LRU Cache. Every request, the requested
prompt_adapters will be loaded (unless they are already loaded)
and least recently used prompt_adapters will
be unloaded if the cache is above capacity."""
_prompt_adapter_manager_cls: Type[
LRUCachePromptAdapterModelManager] = LRUCachePromptAdapterModelManager
def create_prompt_adapter_manager(
self,
model: torch.nn.Module,
) -> Any:
prompt_adapter_manager = create_prompt_adapter_manager(
model,
max_num_seqs=self.max_num_seqs,
max_num_batched_tokens=self.max_num_batched_tokens,
prompt_adapter_config=self.prompt_adapter_config,
prompt_adapter_manager_cls=self._prompt_adapter_manager_cls)
self._adapter_manager: LRUCachePromptAdapterModelManager = (
prompt_adapter_manager)
return prompt_adapter_manager.model
def _apply_adapters(
self, prompt_adapter_requests: Set[PromptAdapterRequest]) -> None:
prompt_adapters_map = {
prompt_adapter_request.prompt_adapter_id: prompt_adapter_request
for prompt_adapter_request in prompt_adapter_requests
if prompt_adapter_request
}
if len(prompt_adapters_map
) > self._adapter_manager.prompt_adapter_slots:
raise RuntimeError(
f"Number of requested prompt_adapters "
f"({len(prompt_adapters_map)}) is greater "
"than the number of GPU prompt_adapter slots "
f"({self._adapter_manager.prompt_adapter_slots}).")
for prompt_adapter in prompt_adapters_map.values():
self.add_adapter(prompt_adapter)
def add_adapter(self,
prompt_adapter_request: PromptAdapterRequest) -> bool:
if prompt_adapter_request.prompt_adapter_id not in self.list_adapters(
):
# Remove before we load the new prompt_adapter to save memory
if len(self._adapter_manager) + 1 > self._adapter_manager.capacity:
self._adapter_manager.remove_oldest_adapter()
prompt_adapter = self._load_adapter(prompt_adapter_request)
loaded = self._adapter_manager.add_adapter(prompt_adapter)
else:
# If the prompt_adapter is already loaded, just touch it to
# update its position in the caches
loaded = self._adapter_manager.get_adapter(
prompt_adapter_request.prompt_adapter_id) is not None
self._adapter_manager.activate_adapter(
prompt_adapter_request.prompt_adapter_id)
return loaded