[LoRA] Add support for pinning lora adapters in the LRU cache (#5603)

tests/lora/test_lora_manager.py

@@ -209,6 +209,34 @@ def test_lora_lru_cache_model_manager(dist_init, dummy_model):
     assert manager.activate_lora(3)
     assert manager.lora_index_to_id[0] == 2
     assert manager.lora_index_to_id[1] == 3
+    assert manager.pin_lora(2)
+    assert manager.lora_index_to_id[0] == 2
+    assert manager.lora_index_to_id[1] == 3
+    assert manager.activate_lora(1)
+    assert manager.lora_index_to_id[0] == 2
+    assert manager.lora_index_to_id[1] == 1
+    assert manager.deactivate_lora(2)
+    assert manager.lora_index_to_id[0] is None
+    assert manager.lora_index_to_id[1] == 1
+    assert manager.activate_lora(3)
+    assert manager.lora_index_to_id[0] == 3
+    assert manager.lora_index_to_id[1] == 1
+    assert manager.pin_lora(3)
+    assert manager.pin_lora(1)
+    with pytest.raises(RuntimeError):
+        assert manager.pin_lora(2)
+    assert manager.lora_index_to_id[0] == 3
+    assert manager.lora_index_to_id[1] == 1
+    with pytest.raises(RuntimeError):
+        assert manager.activate_lora(2)
+
+    assert manager.deactivate_lora(3)
+    assert manager.pin_lora(2)
+    assert manager.lora_index_to_id[0] == 2
+    assert manager.lora_index_to_id[1] == 1
+    assert manager.remove_lora(3)
+    with pytest.raises(ValueError):
+        assert manager.pin_lora(3)
 
 
 def test_lru_lora_model_manager(dist_init, dummy_model):
@@ -288,6 +316,42 @@ def test_lru_lora_model_manager(dist_init, dummy_model):
     assert set(manager.list_loras()) == set()
     assert all(x is None for x in manager.lora_index_to_id)
 
+    # pinning
+    assert manager.add_lora(model_lora3)
+    assert manager.activate_lora(3)
+    assert manager.add_lora(model_lora4)
+    assert manager.activate_lora(4)
+    assert set(manager.list_loras()) == {3, 4}
+    with pytest.raises(ValueError):
+        assert manager.pin_lora(1)
+    assert manager.pin_lora(3)
+    # Remove manually
+    assert manager.remove_lora(3)
+    assert not manager.remove_lora(3)
+
+    assert set(manager.list_loras()) == {4}
+    assert manager.lora_index_to_id[0] is None
+    assert manager.lora_index_to_id[1] == 4
+
+    assert manager.add_lora(model_lora1)
+    assert manager.pin_lora(1)
+    assert manager.add_lora(model_lora2)
+    assert manager.activate_lora(2)
+
+    assert set(manager.list_loras()) == {1, 2}
+    assert manager.lora_index_to_id[0] == 1
+    assert manager.lora_index_to_id[1] == 2
+
+    assert manager.remove_oldest_lora()
+    assert set(manager.list_loras()) == {1}
+    assert manager.lora_index_to_id[0] == 1
+    assert manager.lora_index_to_id[1] is None
+
+    with pytest.raises(RuntimeError):
+        assert manager.remove_oldest_lora()
+
+    assert set(manager.list_loras()) == {1}
+
 
 def test_lru_cache_worker_lora_manager(llama_2_7b_model_extra_embeddings,
                                        sql_lora_files):

vllm/engine/llm_engine.py

@@ -1009,6 +1009,9 @@ class LLMEngine:
     def list_loras(self) -> Set[int]:
         return self.model_executor.list_loras()
 
+    def pin_lora(self, lora_id: int) -> bool:
+        return self.model_executor.pin_lora(lora_id)
+
     def check_health(self) -> None:
         self.model_executor.check_health()
 

vllm/executor/cpu_executor.py

@@ -84,6 +84,9 @@ class CPUExecutor(ExecutorBase):
     def remove_lora(self, lora_id: int) -> bool:
         return self.driver_worker.remove_lora(lora_id)
 
+    def pin_lora(self, lora_id: int) -> bool:
+        return self.driver_worker.pin_lora(lora_id)
+
     def list_loras(self) -> Set[int]:
         return self.driver_worker.list_loras()
 

vllm/executor/distributed_gpu_executor.py

@@ -100,6 +100,13 @@ class DistributedGPUExecutor(GPUExecutor):
             lora_id=lora_id,
         )
 
+    def pin_lora(self, lora_id: int) -> bool:
+        assert lora_id > 0, "lora_id must be greater than 0."
+        return self._run_workers(
+            "pin_lora",
+            lora_id=lora_id,
+        )
+
     def list_loras(self) -> Set[int]:
         return self._run_workers("list_loras")
 

vllm/executor/executor_base.py

@@ -86,6 +86,10 @@ class ExecutorBase(ABC):
     def remove_lora(self, lora_id: int) -> bool:
         raise NotImplementedError
 
+    @abstractmethod
+    def pin_lora(self, lora_id: int) -> bool:
+        raise NotImplementedError  # type: ignore
+
     @abstractmethod
     def list_loras(self) -> Set[int]:
         raise NotImplementedError

vllm/executor/gpu_executor.py

@@ -99,6 +99,10 @@ class GPUExecutor(ExecutorBase):
         assert lora_id > 0, "lora_id must be greater than 0."
         return self.driver_worker.remove_lora(lora_id)
 
+    def pin_lora(self, lora_id: int) -> bool:
+        assert lora_id > 0, "lora_id must be greater than 0."
+        return self.driver_worker.pin_lora(lora_id)
+
     def list_loras(self) -> Set[int]:
         return self.driver_worker.list_loras()
 

vllm/executor/neuron_executor.py

@@ -65,6 +65,9 @@ class NeuronExecutor(ExecutorBase):
     def remove_lora(self, lora_id: int) -> bool:
         return self.driver_worker.remove_lora(lora_id)
 
+    def pin_lora(self, lora_id: int) -> bool:
+        return self.driver_worker.pin_lora(lora_id)
+
     def list_loras(self) -> Set[int]:
         return self.driver_worker.list_loras()
 

vllm/lora/models.py

@@ -525,6 +525,12 @@ class LoRAModelManager:
             self.long_lora_context.offsets_by_lora_id.pop(lora_id, None)
         return bool(self._registered_loras.pop(lora_id, None))
 
+    def pin_lora(self, lora_id: int) -> bool:
+        """Pin a LoRAModel in the manager cache."""
+        raise NotImplementedError(
+            "Pinning is not supported in LoRAModelManager."
+            "Use LRUCacheLoRAModelManager for pinning")  # type: ignore
+
     # TODO see if this can be vectorized
     def _set_lora_mapping(self, mapping: LoRAMapping) -> None:
         (base_indices, sampler_indices, sampler_indices_padded,
@@ -777,6 +783,26 @@ class LRUCacheLoRAModelManager(LoRAModelManager):
             return True
         return False
 
+    def pin_lora(self, lora_id: int) -> bool:
+        """Pin a LoRAModel in the manager cache."""
+        self._pin_lora_in_cpu_cache(lora_id)
+        self._pin_lora_in_gpu_cache(lora_id)
+        return True
+
+    def _pin_lora_in_cpu_cache(self, lora_id: int):
+        try:
+            self._registered_loras.pin(lora_id)
+        except ValueError as err:
+            raise ValueError("Pinning failed. "
+                             f"LoRA {lora_id} is not registered.") from err
+
+    def _pin_lora_in_gpu_cache(self, lora_id: int):
+        if lora_id not in self._active_loras:
+            # move lora to gpu if not already active
+            self.activate_lora(lora_id)
+
+        self._active_loras.pin(lora_id)
+
 
 def create_lora_manager(
         model: nn.Module,

vllm/lora/worker_manager.py

@@ -221,6 +221,9 @@ class WorkerLoRAManager(AbstractWorkerLoRAManager):
     def remove_lora(self, lora_id: int) -> bool:
         return self._lora_manager.remove_lora(lora_id)
 
+    def pin_lora(self, lora_id: int) -> bool:
+        return self._lora_manager.pin_lora(lora_id)
+
     def remove_all_loras(self):
         self._lora_manager.remove_all_loras()
 

vllm/utils.py

@@ -15,7 +15,7 @@ from collections import defaultdict
 from functools import lru_cache, partial, wraps
 from platform import uname
 from typing import (Any, AsyncIterator, Awaitable, Callable, Dict, Generic,
-                    Hashable, List, Optional, OrderedDict, Tuple, TypeVar,
+                    Hashable, List, Optional, OrderedDict, Set, Tuple, TypeVar,
                     Union)
 
 import numpy as np
@@ -44,6 +44,13 @@ K = TypeVar("K")
 T = TypeVar("T")
 
 
+class _Sentinel:
+    ...
+
+
+ALL_PINNED_SENTINEL = _Sentinel()
+
+
 class Device(enum.Enum):
     GPU = enum.auto()
     CPU = enum.auto()
@@ -67,6 +74,7 @@ class LRUCache(Generic[T]):
 
     def __init__(self, capacity: int):
         self.cache: OrderedDict[Hashable, T] = OrderedDict()
+        self.pinned_items: Set[Hashable] = set()
         self.capacity = capacity
 
     def __contains__(self, key: Hashable) -> bool:
@@ -102,14 +110,36 @@ class LRUCache(Generic[T]):
         self.cache.move_to_end(key)
         self._remove_old_if_needed()
 
+    def pin(self, key: Hashable) -> None:
+        """
+        Pins a key in the cache preventing it from being
+        evicted in the LRU order.
+        """
+        if key not in self.cache:
+            raise ValueError(f"Cannot pin key: {key} not in cache.")
+        self.pinned_items.add(key)
+
+    def _unpin(self, key: Hashable) -> None:
+        self.pinned_items.remove(key)
+
     def _on_remove(self, key: Hashable, value: Optional[T]):
         pass
 
-    def remove_oldest(self):
+    def remove_oldest(self, remove_pinned=False):
         if not self.cache:
             return
-        key, value = self.cache.popitem(last=False)
-        self._on_remove(key, value)
+
+        if not remove_pinned:
+            # pop the oldest item in the cache that is not pinned
+            lru_key = next(
+                (key for key in self.cache if key not in self.pinned_items),
+                ALL_PINNED_SENTINEL)
+            if lru_key is ALL_PINNED_SENTINEL:
+                raise RuntimeError("All items are pinned, "
+                                   "cannot remove oldest from the cache.")
+        else:
+            lru_key = next(iter(self.cache))
+        self.pop(lru_key)
 
     def _remove_old_if_needed(self) -> None:
         while len(self.cache) > self.capacity:
@@ -120,13 +150,16 @@ class LRUCache(Generic[T]):
             default_value: Optional[T] = None) -> Optional[T]:
         run_on_remove = key in self.cache
         value: Optional[T] = self.cache.pop(key, default_value)
+        # remove from pinned items
+        if key in self.pinned_items:
+            self._unpin(key)
         if run_on_remove:
             self._on_remove(key, value)
         return value
 
     def clear(self):
         while len(self.cache) > 0:
-            self.remove_oldest()
+            self.remove_oldest(remove_pinned=True)
         self.cache.clear()
 
 

vllm/worker/model_runner.py

@@ -878,6 +878,11 @@ class ModelRunner:
             raise RuntimeError("LoRA is not enabled.")
         return self.lora_manager.remove_lora(lora_id)
 
+    def pin_lora(self, lora_id: int) -> bool:
+        if not self.lora_manager:
+            raise RuntimeError("LoRA is not enabled.")
+        return self.lora_manager.pin_lora(lora_id)
+
     def list_loras(self) -> Set[int]:
         if not self.lora_manager:
             raise RuntimeError("LoRA is not enabled.")

vllm/worker/worker.py

@@ -333,6 +333,9 @@ class Worker(WorkerBase):
     def remove_lora(self, lora_id: int) -> bool:
         return self.model_runner.remove_lora(lora_id)
 
+    def pin_lora(self, lora_id: int) -> bool:
+        return self.model_runner.pin_lora(lora_id)
+
     def list_loras(self) -> Set[int]:
         return self.model_runner.list_loras()
 

vllm/worker/worker_base.py

@@ -70,6 +70,10 @@ class WorkerBase(ABC):
     def remove_lora(self, lora_id: int) -> bool:
         raise NotImplementedError
 
+    @abstractmethod
+    def pin_lora(self, lora_id: int) -> bool:
+        raise NotImplementedError
+
     @abstractmethod
     def list_loras(self) -> Set[int]:
         raise NotImplementedError
@@ -86,6 +90,10 @@ class LoraNotSupportedWorkerBase(WorkerBase):
     def remove_lora(self, lora_id: int) -> bool:
         raise ValueError(f"{type(self)} does not support LoRA")
 
+    def pin_lora(self, lora_id: int) -> bool:
+        return ValueError(
+            f"{type(self)} does not support LoRA")  # type: ignore
+
     def list_loras(self) -> Set[int]:
         raise ValueError(f"{type(self)} does not support LoRA")