TestLLM / litellm /scheduler.py
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import enum
import heapq
from typing import Optional
from pydantic import BaseModel
from litellm import print_verbose
from litellm.caching.caching import DualCache, RedisCache
class SchedulerCacheKeys(enum.Enum):
queue = "scheduler:queue"
default_in_memory_ttl = 5 # cache queue in-memory for 5s when redis cache available
class DefaultPriorities(enum.Enum):
High = 0
Medium = 128
Low = 255
class FlowItem(BaseModel):
priority: int # Priority between 0 and 255
request_id: str
model_name: str
class Scheduler:
cache: DualCache
def __init__(
self,
polling_interval: Optional[float] = None,
redis_cache: Optional[RedisCache] = None,
):
"""
polling_interval: float or null - frequency of polling queue. Default is 3ms.
"""
self.queue: list = []
default_in_memory_ttl: Optional[float] = None
if redis_cache is not None:
# if redis-cache available frequently poll that instead of using in-memory.
default_in_memory_ttl = SchedulerCacheKeys.default_in_memory_ttl.value
self.cache = DualCache(
redis_cache=redis_cache, default_in_memory_ttl=default_in_memory_ttl
)
self.polling_interval = polling_interval or 0.03 # default to 3ms
async def add_request(self, request: FlowItem):
# We use the priority directly, as lower values indicate higher priority
# get the queue
queue = await self.get_queue(model_name=request.model_name)
# update the queue
heapq.heappush(queue, (request.priority, request.request_id))
# save the queue
await self.save_queue(queue=queue, model_name=request.model_name)
async def poll(self, id: str, model_name: str, health_deployments: list) -> bool:
"""
Return if request can be processed.
Returns:
- True:
* If healthy deployments are available
* OR If request at the top of queue
- False:
* If no healthy deployments available
* AND request not at the top of queue
"""
queue = await self.get_queue(model_name=model_name)
if not queue:
raise Exception(
"Incorrectly setup. Queue is invalid. Queue={}".format(queue)
)
# ------------
# Setup values
# ------------
print_verbose(f"len(health_deployments): {len(health_deployments)}")
if len(health_deployments) == 0:
print_verbose(f"queue: {queue}, seeking id={id}")
# Check if the id is at the top of the heap
if queue[0][1] == id:
# Remove the item from the queue
heapq.heappop(queue)
print_verbose(f"Popped id: {id}")
return True
else:
return False
return True
async def peek(self, id: str, model_name: str, health_deployments: list) -> bool:
"""Return if the id is at the top of the queue. Don't pop the value from heap."""
queue = await self.get_queue(model_name=model_name)
if not queue:
raise Exception(
"Incorrectly setup. Queue is invalid. Queue={}".format(queue)
)
# ------------
# Setup values
# ------------
# Check if the id is at the top of the heap
if queue[0][1] == id:
return True
return False
def get_queue_status(self):
"""Get the status of items in the queue"""
return self.queue
async def get_queue(self, model_name: str) -> list:
"""
Return a queue for that specific model group
"""
if self.cache is not None:
_cache_key = "{}:{}".format(SchedulerCacheKeys.queue.value, model_name)
response = await self.cache.async_get_cache(key=_cache_key)
if response is None or not isinstance(response, list):
return []
elif isinstance(response, list):
return response
return self.queue
async def save_queue(self, queue: list, model_name: str) -> None:
"""
Save the updated queue of the model group
"""
if self.cache is not None:
_cache_key = "{}:{}".format(SchedulerCacheKeys.queue.value, model_name)
await self.cache.async_set_cache(key=_cache_key, value=queue)
return None