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