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https://github.com/BerriAI/litellm.git
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bump: version 0.13.6.dev3 → 0.13.6
This commit is contained in:
Krrish Dholakia
parent
28b8321c5a
commit
632533f2e2
9 changed files with 126 additions and 143 deletions
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@ -1,8 +1,8 @@
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from datetime import datetime
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from typing import Dict, List, Optional, Union
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import random, threading, time
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import litellm
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import logging
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class Router:
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"""
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@ -16,8 +16,6 @@ class Router:
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"api_version": <your-api-version>,
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"api_base": <your-api-base>
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},
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"tpm": <your-model-tpm>, e.g. 240000
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"rpm": <your-model-rpm>, e.g. 1800
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}]
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router = Router(model_list=model_list)
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@ -34,6 +32,11 @@ class Router:
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cache_responses: bool = False) -> None:
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if model_list:
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self.set_model_list(model_list)
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self.healthy_deployments = []
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### HEALTH CHECK THREAD ### - commenting out as further testing required
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# self._start_health_check_thread()
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### CACHING ###
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if redis_host is not None and redis_port is not None and redis_password is not None:
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cache_config = {
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'type': 'redis',
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@ -45,11 +48,80 @@ class Router:
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cache_config = {
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"type": "local"
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}
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self.cache = litellm.Cache(cache_config) # use Redis for tracking load balancing
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if cache_responses:
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litellm.cache = litellm.Cache(**cache_config) # use Redis for caching completion requests
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self.cache_responses = cache_responses
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litellm.success_callback = [self.deployment_callback]
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def _start_health_check_thread(self):
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"""
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Starts a separate thread to perform health checks periodically.
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"""
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health_check_thread = threading.Thread(target=self._perform_health_checks, daemon=True)
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health_check_thread.start()
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def _perform_health_checks(self):
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"""
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Periodically performs health checks on the servers.
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Updates the list of healthy servers accordingly.
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"""
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while True:
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self.healthy_deployments = self._health_check()
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# Adjust the time interval based on your needs
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time.sleep(15)
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def _health_check(self):
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"""
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Performs a health check on the deployments
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Returns the list of healthy deployments
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"""
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healthy_deployments = []
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for deployment in self.model_list:
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litellm_args = deployment["litellm_params"]
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try:
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start_time = time.time()
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litellm.completion(messages=[{"role": "user", "content": ""}], max_tokens=1, **litellm_args) # hit the server with a blank message to see how long it takes to respond
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end_time = time.time()
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response_time = end_time - start_time
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logging.debug(f"response_time: {response_time}")
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healthy_deployments.append((deployment, response_time))
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healthy_deployments.sort(key=lambda x: x[1])
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except Exception as e:
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pass
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return healthy_deployments
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def set_model_list(self, model_list: list):
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self.model_list = model_list
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self.model_names = [m["model_name"] for m in model_list]
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def get_model_names(self):
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return self.model_names
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def get_available_deployment(self,
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model: str,
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messages: Optional[List[Dict[str, str]]] = None,
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input: Optional[Union[str, List]] = None):
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"""
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Returns the deployment with the shortest queue
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"""
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### COMMENTING OUT AS IT NEEDS FURTHER TESTING
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# logging.debug(f"self.healthy_deployments: {self.healthy_deployments}")
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# if len(self.healthy_deployments) > 0:
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# for item in self.healthy_deployments:
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# print(f"item: {item}")
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# if item[0]["model_name"] == model: # first one in queue will be the one with the most availability
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# return item
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# else:
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potential_deployments = []
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for item in self.model_list:
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if item["model_name"] == model:
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potential_deployments.append(item)
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item = random.choice(potential_deployments)
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return item
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raise ValueError("No models available.")
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### COMPLETION + EMBEDDING FUNCTIONS
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def completion(self,
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model: str,
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@ -66,8 +138,12 @@ class Router:
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deployment = self.get_available_deployment(model=model, messages=messages)
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data = deployment["litellm_params"]
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# call via litellm.completion()
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# return litellm.completion(**{**data, "messages": messages, "caching": self.cache_responses, **kwargs})
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# litellm.set_verbose = True
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return litellm.completion(**{**data, "messages": messages, "caching": self.cache_responses, **kwargs})
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async def acompletion(self,
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model: str,
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messages: List[Dict[str, str]],
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@ -116,128 +192,4 @@ class Router:
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deployment = self.get_available_deployment(model=model, input=input)
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data = deployment["litellm_params"]
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return await litellm.aembedding(**{**data, "input": input, "caching": self.cache_responses, **kwargs})
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def set_model_list(self, model_list: list):
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self.model_list = model_list
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self.model_names = [m["model_name"] for m in model_list]
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def get_model_names(self):
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return self.model_names
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def deployment_callback(
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self,
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kwargs, # kwargs to completion
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completion_response, # response from completion
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start_time, end_time # start/end time
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):
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"""
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Function LiteLLM submits a callback to after a successful
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completion. Purpose of this is ti update TPM/RPM usage per model
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"""
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model_name = kwargs.get('model', None) # i.e. gpt35turbo
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custom_llm_provider = kwargs.get("litellm_params", {}).get('custom_llm_provider', None) # i.e. azure
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if custom_llm_provider:
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model_name = f"{custom_llm_provider}/{model_name}"
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total_tokens = completion_response['usage']['total_tokens']
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self._set_deployment_usage(model_name, total_tokens)
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def get_available_deployment(self,
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model: str,
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messages: Optional[List[Dict[str, str]]] = None,
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input: Optional[Union[str, List]] = None):
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"""
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Returns a deployment with the lowest TPM/RPM usage.
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"""
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# get list of potential deployments
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potential_deployments = []
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for item in self.model_list:
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if item["model_name"] == model:
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potential_deployments.append(item)
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# set first model as current model to calculate token count
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deployment = potential_deployments[0]
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# get encoding
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token_count = 0
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if messages is not None:
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token_count = litellm.token_counter(model=deployment["model_name"], messages=messages)
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elif input is not None:
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if isinstance(input, List):
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input_text = "".join(text for text in input)
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else:
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input_text = input
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token_count = litellm.token_counter(model=deployment["model_name"], text=input_text)
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# -----------------------
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# Find lowest used model
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# ----------------------
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lowest_tpm = float("inf")
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deployment = None
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# Go through all the models to get tpm, rpm
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for item in potential_deployments:
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item_tpm, item_rpm = self._get_deployment_usage(deployment_name=item["litellm_params"]["model"])
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if item_tpm == 0:
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return item
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elif item_tpm + token_count > item["tpm"] or item_rpm + 1 >= item["rpm"]:
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continue
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elif item_tpm < lowest_tpm:
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lowest_tpm = item_tpm
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deployment = item
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# if none, raise exception
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if deployment is None:
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raise ValueError("No models available.")
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# return model
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return deployment
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def _get_deployment_usage(
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self,
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deployment_name: str
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):
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# ------------
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# Setup values
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# ------------
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current_minute = datetime.now().strftime("%H-%M")
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tpm_key = f'{deployment_name}:tpm:{current_minute}'
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rpm_key = f'{deployment_name}:rpm:{current_minute}'
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# ------------
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# Return usage
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# ------------
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tpm = self.cache.get_cache(cache_key=tpm_key) or 0
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rpm = self.cache.get_cache(cache_key=rpm_key) or 0
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return int(tpm), int(rpm)
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def increment(self, key: str, increment_value: int):
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# get value
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cached_value = self.cache.get_cache(cache_key=key)
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# update value
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try:
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cached_value = cached_value + increment_value
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except:
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cached_value = increment_value
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# save updated value
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self.cache.add_cache(result=cached_value, cache_key=key, ttl=self.default_cache_time_seconds)
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def _set_deployment_usage(
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self,
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model_name: str,
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total_tokens: int
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):
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# ------------
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# Setup values
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# ------------
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current_minute = datetime.now().strftime("%H-%M")
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tpm_key = f'{model_name}:tpm:{current_minute}'
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rpm_key = f'{model_name}:rpm:{current_minute}'
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# ------------
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# Update usage
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# ------------
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self.increment(tpm_key, total_tokens)
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self.increment(rpm_key, 1)
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return await litellm.aembedding(**{**data, "input": input, "caching": self.cache_responses, **kwargs})
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