mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 02:34:29 +00:00
767 lines
No EOL
32 KiB
Python
767 lines
No EOL
32 KiB
Python
# +-----------------------------------------------+
|
|
# | |
|
|
# | Give Feedback / Get Help |
|
|
# | https://github.com/BerriAI/litellm/issues/new |
|
|
# | |
|
|
# +-----------------------------------------------+
|
|
#
|
|
# Thank you ! We ❤️ you! - Krrish & Ishaan
|
|
|
|
from datetime import datetime
|
|
from typing import Dict, List, Optional, Union, Literal
|
|
import random, threading, time, traceback
|
|
import litellm, openai
|
|
from litellm.caching import RedisCache, InMemoryCache, DualCache
|
|
import logging, asyncio
|
|
import inspect, concurrent
|
|
from openai import AsyncOpenAI
|
|
|
|
class Router:
|
|
"""
|
|
Example usage:
|
|
```python
|
|
from litellm import Router
|
|
model_list = [
|
|
{
|
|
"model_name": "azure-gpt-3.5-turbo", # model alias
|
|
"litellm_params": { # params for litellm completion/embedding call
|
|
"model": "azure/<your-deployment-name-1>",
|
|
"api_key": <your-api-key>,
|
|
"api_version": <your-api-version>,
|
|
"api_base": <your-api-base>
|
|
},
|
|
},
|
|
{
|
|
"model_name": "azure-gpt-3.5-turbo", # model alias
|
|
"litellm_params": { # params for litellm completion/embedding call
|
|
"model": "azure/<your-deployment-name-2>",
|
|
"api_key": <your-api-key>,
|
|
"api_version": <your-api-version>,
|
|
"api_base": <your-api-base>
|
|
},
|
|
},
|
|
{
|
|
"model_name": "openai-gpt-3.5-turbo", # model alias
|
|
"litellm_params": { # params for litellm completion/embedding call
|
|
"model": "gpt-3.5-turbo",
|
|
"api_key": <your-api-key>,
|
|
},
|
|
]
|
|
|
|
router = Router(model_list=model_list, fallbacks=[{"azure-gpt-3.5-turbo": "openai-gpt-3.5-turbo"}])
|
|
```
|
|
"""
|
|
model_names: List = []
|
|
cache_responses: bool = False
|
|
default_cache_time_seconds: int = 1 * 60 * 60 # 1 hour
|
|
num_retries: int = 0
|
|
tenacity = None
|
|
|
|
def __init__(self,
|
|
model_list: Optional[list] = None,
|
|
redis_host: Optional[str] = None,
|
|
redis_port: Optional[int] = None,
|
|
redis_password: Optional[str] = None,
|
|
cache_responses: bool = False,
|
|
num_retries: int = 0,
|
|
timeout: float = 600,
|
|
default_litellm_params = {}, # default params for Router.chat.completion.create
|
|
set_verbose: bool = False,
|
|
fallbacks: List = [],
|
|
context_window_fallbacks: List = [],
|
|
routing_strategy: Literal["simple-shuffle", "least-busy", "usage-based-routing", "latency-based-routing"] = "simple-shuffle") -> None:
|
|
|
|
if model_list:
|
|
self.set_model_list(model_list)
|
|
self.healthy_deployments: List = self.model_list
|
|
self.deployment_latency_map = {}
|
|
self.cooldown_deployments: dict = {} # {"gpt-3.5-turbo": time.time() when it failed / needed a cooldown}
|
|
for m in model_list:
|
|
self.deployment_latency_map[m["litellm_params"]["model"]] = 0
|
|
|
|
self.num_retries = num_retries
|
|
self.set_verbose = set_verbose
|
|
self.timeout = timeout
|
|
self.routing_strategy = routing_strategy
|
|
self.fallbacks = fallbacks
|
|
self.context_window_fallbacks = context_window_fallbacks
|
|
|
|
# make Router.chat.completions.create compatible for openai.chat.completions.create
|
|
self.chat = litellm.Chat(params=default_litellm_params)
|
|
|
|
# default litellm args
|
|
self.default_litellm_params = default_litellm_params
|
|
self.default_litellm_params["timeout"] = timeout
|
|
|
|
|
|
### HEALTH CHECK THREAD ###
|
|
if self.routing_strategy == "least-busy":
|
|
self._start_health_check_thread()
|
|
|
|
### CACHING ###
|
|
redis_cache = None
|
|
if redis_host is not None and redis_port is not None and redis_password is not None:
|
|
cache_config = {
|
|
'type': 'redis',
|
|
'host': redis_host,
|
|
'port': redis_port,
|
|
'password': redis_password
|
|
}
|
|
redis_cache = RedisCache(host=redis_host, port=redis_port, password=redis_password)
|
|
else: # use an in-memory cache
|
|
cache_config = {
|
|
"type": "local"
|
|
}
|
|
if cache_responses:
|
|
litellm.cache = litellm.Cache(**cache_config) # use Redis for caching completion requests
|
|
self.cache_responses = cache_responses
|
|
self.cache = DualCache(redis_cache=redis_cache) # use a dual cache (Redis+In-Memory) for tracking cooldowns, usage, etc.
|
|
## USAGE TRACKING ##
|
|
if isinstance(litellm.success_callback, list):
|
|
litellm.success_callback.append(self.deployment_callback)
|
|
else:
|
|
litellm.success_callback = [self.deployment_callback]
|
|
|
|
if isinstance(litellm.failure_callback, list):
|
|
litellm.failure_callback.append(self.deployment_callback_on_failure)
|
|
else:
|
|
litellm.failure_callback = [self.deployment_callback_on_failure]
|
|
|
|
|
|
### COMPLETION + EMBEDDING FUNCTIONS
|
|
|
|
def completion(self,
|
|
model: str,
|
|
messages: List[Dict[str, str]],
|
|
**kwargs):
|
|
"""
|
|
Example usage:
|
|
response = router.completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hey, how's it going?"}]
|
|
"""
|
|
try:
|
|
kwargs["model"] = model
|
|
kwargs["messages"] = messages
|
|
kwargs["original_function"] = self._completion
|
|
kwargs["num_retries"] = self.num_retries
|
|
with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:
|
|
# Submit the function to the executor with a timeout
|
|
future = executor.submit(self.function_with_fallbacks, **kwargs)
|
|
response = future.result(timeout=self.timeout)
|
|
|
|
return response
|
|
except Exception as e:
|
|
raise e
|
|
|
|
def _completion(
|
|
self,
|
|
model: str,
|
|
messages: List[Dict[str, str]],
|
|
**kwargs):
|
|
|
|
try:
|
|
# pick the one that is available (lowest TPM/RPM)
|
|
deployment = self.get_available_deployment(model=model, messages=messages)
|
|
data = deployment["litellm_params"]
|
|
for k, v in self.default_litellm_params.items():
|
|
if k not in data: # prioritize model-specific params > default router params
|
|
data[k] = v
|
|
|
|
self.print_verbose(f"completion model: {data['model']}")
|
|
return litellm.completion(**{**data, "messages": messages, "caching": self.cache_responses, **kwargs})
|
|
except Exception as e:
|
|
raise e
|
|
|
|
async def acompletion(self,
|
|
model: str,
|
|
messages: List[Dict[str, str]],
|
|
**kwargs):
|
|
try:
|
|
kwargs["model"] = model
|
|
kwargs["messages"] = messages
|
|
kwargs["original_function"] = self._acompletion
|
|
kwargs["num_retries"] = self.num_retries
|
|
|
|
# Use asyncio.timeout to enforce the timeout
|
|
async with asyncio.timeout(self.timeout):
|
|
response = await self.async_function_with_fallbacks(**kwargs)
|
|
|
|
return response
|
|
except Exception as e:
|
|
raise e
|
|
|
|
async def _acompletion(
|
|
self,
|
|
model: str,
|
|
messages: List[Dict[str, str]],
|
|
**kwargs):
|
|
try:
|
|
deployment = self.get_available_deployment(model=model, messages=messages)
|
|
data = deployment["litellm_params"]
|
|
for k, v in self.default_litellm_params.items():
|
|
if k not in data: # prioritize model-specific params > default router params
|
|
data[k] = v
|
|
self.print_verbose(f"acompletion model: {data['model']}")
|
|
|
|
response = await litellm.acompletion(**{**data, "messages": messages, "caching": self.cache_responses, **kwargs})
|
|
return response
|
|
except Exception as e:
|
|
raise e
|
|
|
|
def text_completion(self,
|
|
model: str,
|
|
prompt: str,
|
|
is_retry: Optional[bool] = False,
|
|
is_fallback: Optional[bool] = False,
|
|
is_async: Optional[bool] = False,
|
|
**kwargs):
|
|
try:
|
|
messages=[{"role": "user", "content": prompt}]
|
|
# pick the one that is available (lowest TPM/RPM)
|
|
deployment = self.get_available_deployment(model=model, messages=messages)
|
|
|
|
data = deployment["litellm_params"]
|
|
for k, v in self.default_litellm_params.items():
|
|
if k not in data: # prioritize model-specific params > default router params
|
|
data[k] = v
|
|
# call via litellm.completion()
|
|
return litellm.text_completion(**{**data, "prompt": prompt, "caching": self.cache_responses, **kwargs}) # type: ignore
|
|
except Exception as e:
|
|
if self.num_retries > 0:
|
|
kwargs["model"] = model
|
|
kwargs["messages"] = messages
|
|
kwargs["original_exception"] = e
|
|
kwargs["original_function"] = self.completion
|
|
return self.function_with_retries(**kwargs)
|
|
else:
|
|
raise e
|
|
|
|
def embedding(self,
|
|
model: str,
|
|
input: Union[str, List],
|
|
is_async: Optional[bool] = False,
|
|
**kwargs) -> Union[List[float], None]:
|
|
# pick the one that is available (lowest TPM/RPM)
|
|
deployment = self.get_available_deployment(model=model, input=input)
|
|
|
|
data = deployment["litellm_params"]
|
|
for k, v in self.default_litellm_params.items():
|
|
if k not in data: # prioritize model-specific params > default router params
|
|
data[k] = v
|
|
# call via litellm.embedding()
|
|
return litellm.embedding(**{**data, "input": input, "caching": self.cache_responses, **kwargs})
|
|
|
|
async def aembedding(self,
|
|
model: str,
|
|
input: Union[str, List],
|
|
is_async: Optional[bool] = True,
|
|
**kwargs) -> Union[List[float], None]:
|
|
# pick the one that is available (lowest TPM/RPM)
|
|
deployment = self.get_available_deployment(model=model, input=input)
|
|
|
|
data = deployment["litellm_params"]
|
|
for k, v in self.default_litellm_params.items():
|
|
if k not in data: # prioritize model-specific params > default router params
|
|
data[k] = v
|
|
return await litellm.aembedding(**{**data, "input": input, "caching": self.cache_responses, **kwargs})
|
|
|
|
async def async_function_with_fallbacks(self, *args, **kwargs):
|
|
"""
|
|
Try calling the function_with_retries
|
|
If it fails after num_retries, fall back to another model group
|
|
"""
|
|
model_group = kwargs.get("model")
|
|
try:
|
|
response = await self.async_function_with_retries(*args, **kwargs)
|
|
self.print_verbose(f'Async Response: {response}')
|
|
return response
|
|
except Exception as e:
|
|
self.print_verbose(f"An exception occurs")
|
|
original_exception = e
|
|
try:
|
|
self.print_verbose(f"Trying to fallback b/w models")
|
|
if isinstance(e, litellm.ContextWindowExceededError):
|
|
fallback_model_group = None
|
|
for item in self.context_window_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}]
|
|
if list(item.keys())[0] == model_group:
|
|
fallback_model_group = item[model_group]
|
|
break
|
|
|
|
if fallback_model_group is None:
|
|
raise original_exception
|
|
|
|
for mg in fallback_model_group:
|
|
"""
|
|
Iterate through the model groups and try calling that deployment
|
|
"""
|
|
try:
|
|
kwargs["model"] = mg
|
|
response = await self.async_function_with_retries(*args, **kwargs)
|
|
return response
|
|
except Exception as e:
|
|
pass
|
|
else:
|
|
self.print_verbose(f"inside model fallbacks: {self.fallbacks}")
|
|
for item in self.fallbacks:
|
|
if list(item.keys())[0] == model_group:
|
|
fallback_model_group = item[model_group]
|
|
break
|
|
for mg in fallback_model_group:
|
|
"""
|
|
Iterate through the model groups and try calling that deployment
|
|
"""
|
|
try:
|
|
kwargs["model"] = mg
|
|
response = await self.async_function_with_retries(*args, **kwargs)
|
|
return response
|
|
except Exception as e:
|
|
pass
|
|
except Exception as e:
|
|
self.print_verbose(f"An exception occurred - {str(e)}")
|
|
traceback.print_exc()
|
|
raise original_exception
|
|
|
|
async def async_function_with_retries(self, *args, **kwargs):
|
|
self.print_verbose(f"Inside async function with retries: args - {args}; kwargs - {kwargs}")
|
|
backoff_factor = 1
|
|
original_function = kwargs.pop("original_function")
|
|
num_retries = kwargs.pop("num_retries")
|
|
try:
|
|
# if the function call is successful, no exception will be raised and we'll break out of the loop
|
|
response = await original_function(*args, **kwargs)
|
|
return response
|
|
except Exception as e:
|
|
for current_attempt in range(num_retries):
|
|
num_retries -= 1 # decrement the number of retries
|
|
self.print_verbose(f"retrying request. Current attempt - {current_attempt}; num retries: {num_retries}")
|
|
try:
|
|
# if the function call is successful, no exception will be raised and we'll break out of the loop
|
|
response = await original_function(*args, **kwargs)
|
|
if inspect.iscoroutinefunction(response): # async errors are often returned as coroutines
|
|
response = await response
|
|
return response
|
|
|
|
except openai.RateLimitError as e:
|
|
if num_retries > 0:
|
|
# on RateLimitError we'll wait for an exponential time before trying again
|
|
await asyncio.sleep(backoff_factor)
|
|
|
|
# increase backoff factor for next run
|
|
backoff_factor *= 2
|
|
else:
|
|
raise e
|
|
|
|
except Exception as e:
|
|
# for any other exception types, immediately retry
|
|
if num_retries > 0:
|
|
pass
|
|
else:
|
|
raise e
|
|
if self.num_retries == 0:
|
|
raise e
|
|
|
|
def function_with_fallbacks(self, *args, **kwargs):
|
|
"""
|
|
Try calling the function_with_retries
|
|
If it fails after num_retries, fall back to another model group
|
|
"""
|
|
model_group = kwargs.get("model")
|
|
|
|
try:
|
|
response = self.function_with_retries(*args, **kwargs)
|
|
self.print_verbose(f'Response: {response}')
|
|
return response
|
|
except Exception as e:
|
|
original_exception = e
|
|
self.print_verbose(f"An exception occurs{original_exception}")
|
|
try:
|
|
self.print_verbose(f"Trying to fallback b/w models. Initial model group: {model_group}")
|
|
self.print_verbose(f"Type of exception: {type(e)}; error_message: {str(e)}")
|
|
if isinstance(e, litellm.ContextWindowExceededError):
|
|
self.print_verbose(f"inside context window fallbacks: {self.context_window_fallbacks}")
|
|
fallback_model_group = None
|
|
for item in self.context_window_fallbacks: # [{"gpt-3.5-turbo": ["gpt-4"]}]
|
|
if list(item.keys())[0] == model_group:
|
|
fallback_model_group = item[model_group]
|
|
break
|
|
|
|
if fallback_model_group is None:
|
|
raise original_exception
|
|
|
|
for mg in fallback_model_group:
|
|
"""
|
|
Iterate through the model groups and try calling that deployment
|
|
"""
|
|
try:
|
|
kwargs["model"] = mg
|
|
response = self.function_with_fallbacks(*args, **kwargs)
|
|
return response
|
|
except Exception as e:
|
|
pass
|
|
else:
|
|
self.print_verbose(f"inside model fallbacks: {self.fallbacks}")
|
|
fallback_model_group = None
|
|
for item in self.fallbacks:
|
|
if list(item.keys())[0] == model_group:
|
|
fallback_model_group = item[model_group]
|
|
break
|
|
|
|
if fallback_model_group is None:
|
|
raise original_exception
|
|
|
|
for mg in fallback_model_group:
|
|
"""
|
|
Iterate through the model groups and try calling that deployment
|
|
"""
|
|
try:
|
|
kwargs["model"] = mg
|
|
response = self.function_with_fallbacks(*args, **kwargs)
|
|
return response
|
|
except Exception as e:
|
|
pass
|
|
except Exception as e:
|
|
self.print_verbose(f"An exception occurred - {str(e)}")
|
|
traceback.print_exc()
|
|
raise original_exception
|
|
|
|
|
|
|
|
def function_with_retries(self, *args, **kwargs):
|
|
"""
|
|
Try calling the model 3 times. Shuffle between available deployments.
|
|
"""
|
|
self.print_verbose(f"Inside function with retries: args - {args}; kwargs - {kwargs}")
|
|
backoff_factor = 1
|
|
original_function = kwargs.pop("original_function")
|
|
num_retries = kwargs.pop("num_retries")
|
|
try:
|
|
# if the function call is successful, no exception will be raised and we'll break out of the loop
|
|
response = original_function(*args, **kwargs)
|
|
return response
|
|
except Exception as e:
|
|
self.print_verbose(f"num retries in function with retries: {num_retries}")
|
|
for current_attempt in range(num_retries):
|
|
num_retries -= 1 # decrement the number of retries
|
|
self.print_verbose(f"retrying request. Current attempt - {current_attempt}; num retries: {num_retries}")
|
|
try:
|
|
# if the function call is successful, no exception will be raised and we'll break out of the loop
|
|
response = original_function(*args, **kwargs)
|
|
return response
|
|
|
|
except openai.RateLimitError as e:
|
|
if num_retries > 0:
|
|
# on RateLimitError we'll wait for an exponential time before trying again
|
|
time.sleep(backoff_factor)
|
|
|
|
# increase backoff factor for next run
|
|
backoff_factor *= 2
|
|
else:
|
|
raise e
|
|
|
|
except Exception as e:
|
|
# for any other exception types, immediately retry
|
|
if num_retries > 0:
|
|
pass
|
|
else:
|
|
raise e
|
|
if self.num_retries == 0:
|
|
raise e
|
|
|
|
### HELPER FUNCTIONS
|
|
|
|
def deployment_callback(
|
|
self,
|
|
kwargs, # kwargs to completion
|
|
completion_response, # response from completion
|
|
start_time, end_time # start/end time
|
|
):
|
|
"""
|
|
Function LiteLLM submits a callback to after a successful
|
|
completion. Purpose of this is to update TPM/RPM usage per model
|
|
"""
|
|
model_name = kwargs.get('model', None) # i.e. gpt35turbo
|
|
custom_llm_provider = kwargs.get("litellm_params", {}).get('custom_llm_provider', None) # i.e. azure
|
|
if custom_llm_provider:
|
|
model_name = f"{custom_llm_provider}/{model_name}"
|
|
if kwargs["stream"] is True:
|
|
if kwargs.get("complete_streaming_response"):
|
|
total_tokens = kwargs.get("complete_streaming_response")['usage']['total_tokens']
|
|
self._set_deployment_usage(model_name, total_tokens)
|
|
else:
|
|
total_tokens = completion_response['usage']['total_tokens']
|
|
self._set_deployment_usage(model_name, total_tokens)
|
|
|
|
self.deployment_latency_map[model_name] = (end_time - start_time).total_seconds()
|
|
|
|
def deployment_callback_on_failure(
|
|
self,
|
|
kwargs, # kwargs to completion
|
|
completion_response, # response from completion
|
|
start_time, end_time # start/end time
|
|
):
|
|
try:
|
|
model_name = kwargs.get('model', None) # i.e. gpt35turbo
|
|
custom_llm_provider = kwargs.get("litellm_params", {}).get('custom_llm_provider', None) # i.e. azure
|
|
if custom_llm_provider:
|
|
model_name = f"{custom_llm_provider}/{model_name}"
|
|
|
|
self._set_cooldown_deployments(model_name)
|
|
except Exception as e:
|
|
raise e
|
|
|
|
def _set_cooldown_deployments(self,
|
|
deployment: str):
|
|
"""
|
|
Add a model to the list of models being cooled down for that minute
|
|
"""
|
|
|
|
current_minute = datetime.now().strftime("%H-%M")
|
|
# get the current cooldown list for that minute
|
|
cooldown_key = f"{current_minute}:cooldown_models" # group cooldown models by minute to reduce number of redis calls
|
|
cached_value = self.cache.get_cache(key=cooldown_key)
|
|
|
|
self.print_verbose(f"adding {deployment} to cooldown models")
|
|
# update value
|
|
try:
|
|
if deployment in cached_value:
|
|
pass
|
|
else:
|
|
cached_value = cached_value + [deployment]
|
|
# save updated value
|
|
self.cache.set_cache(value=cached_value, key=cooldown_key, ttl=60)
|
|
except:
|
|
cached_value = [deployment]
|
|
# save updated value
|
|
self.cache.set_cache(value=cached_value, key=cooldown_key, ttl=60)
|
|
|
|
def _get_cooldown_deployments(self):
|
|
"""
|
|
Get the list of models being cooled down for this minute
|
|
"""
|
|
current_minute = datetime.now().strftime("%H-%M")
|
|
# get the current cooldown list for that minute
|
|
cooldown_key = f"{current_minute}:cooldown_models"
|
|
|
|
# ----------------------
|
|
# Return cooldown models
|
|
# ----------------------
|
|
cooldown_models = self.cache.get_cache(key=cooldown_key) or []
|
|
|
|
self.print_verbose(f"retrieve cooldown models: {cooldown_models}")
|
|
return cooldown_models
|
|
|
|
def get_usage_based_available_deployment(self,
|
|
model: str,
|
|
messages: Optional[List[Dict[str, str]]] = None,
|
|
input: Optional[Union[str, List]] = None):
|
|
"""
|
|
Returns a deployment with the lowest TPM/RPM usage.
|
|
"""
|
|
# get list of potential deployments
|
|
potential_deployments = []
|
|
for item in self.model_list:
|
|
if item["model_name"] == model:
|
|
potential_deployments.append(item)
|
|
|
|
# get current call usage
|
|
token_count = 0
|
|
if messages is not None:
|
|
token_count = litellm.token_counter(model=model, messages=messages)
|
|
elif input is not None:
|
|
if isinstance(input, List):
|
|
input_text = "".join(text for text in input)
|
|
else:
|
|
input_text = input
|
|
token_count = litellm.token_counter(model=model, text=input_text)
|
|
|
|
# -----------------------
|
|
# Find lowest used model
|
|
# ----------------------
|
|
lowest_tpm = float("inf")
|
|
deployment = None
|
|
|
|
# return deployment with lowest tpm usage
|
|
for item in potential_deployments:
|
|
item_tpm, item_rpm = self._get_deployment_usage(deployment_name=item["litellm_params"]["model"])
|
|
|
|
if item_tpm == 0:
|
|
return item
|
|
elif ("tpm" in item and item_tpm + token_count > item["tpm"]
|
|
or "rpm" in item and item_rpm + 1 >= item["rpm"]): # if user passed in tpm / rpm in the model_list
|
|
continue
|
|
elif item_tpm < lowest_tpm:
|
|
lowest_tpm = item_tpm
|
|
deployment = item
|
|
|
|
# if none, raise exception
|
|
if deployment is None:
|
|
raise ValueError("No models available.")
|
|
|
|
# return model
|
|
return deployment
|
|
|
|
def _get_deployment_usage(
|
|
self,
|
|
deployment_name: str
|
|
):
|
|
# ------------
|
|
# Setup values
|
|
# ------------
|
|
current_minute = datetime.now().strftime("%H-%M")
|
|
tpm_key = f'{deployment_name}:tpm:{current_minute}'
|
|
rpm_key = f'{deployment_name}:rpm:{current_minute}'
|
|
|
|
# ------------
|
|
# Return usage
|
|
# ------------
|
|
tpm = self.cache.get_cache(key=tpm_key) or 0
|
|
rpm = self.cache.get_cache(key=rpm_key) or 0
|
|
|
|
return int(tpm), int(rpm)
|
|
|
|
def increment(self, key: str, increment_value: int):
|
|
# get value
|
|
cached_value = self.cache.get_cache(key=key)
|
|
# update value
|
|
try:
|
|
cached_value = cached_value + increment_value
|
|
except:
|
|
cached_value = increment_value
|
|
# save updated value
|
|
self.cache.set_cache(value=cached_value, key=key, ttl=self.default_cache_time_seconds)
|
|
|
|
def _set_deployment_usage(
|
|
self,
|
|
model_name: str,
|
|
total_tokens: int
|
|
):
|
|
# ------------
|
|
# Setup values
|
|
# ------------
|
|
current_minute = datetime.now().strftime("%H-%M")
|
|
tpm_key = f'{model_name}:tpm:{current_minute}'
|
|
rpm_key = f'{model_name}:rpm:{current_minute}'
|
|
|
|
# ------------
|
|
# Update usage
|
|
# ------------
|
|
self.increment(tpm_key, total_tokens)
|
|
self.increment(rpm_key, 1)
|
|
|
|
def _start_health_check_thread(self):
|
|
"""
|
|
Starts a separate thread to perform health checks periodically.
|
|
"""
|
|
health_check_thread = threading.Thread(target=self._perform_health_checks, daemon=True)
|
|
health_check_thread.start()
|
|
|
|
def _perform_health_checks(self):
|
|
"""
|
|
Periodically performs health checks on the servers.
|
|
Updates the list of healthy servers accordingly.
|
|
"""
|
|
while True:
|
|
self.healthy_deployments = self._health_check()
|
|
# Adjust the time interval based on your needs
|
|
time.sleep(15)
|
|
|
|
def _health_check(self):
|
|
"""
|
|
Performs a health check on the deployments
|
|
Returns the list of healthy deployments
|
|
"""
|
|
healthy_deployments = []
|
|
for deployment in self.model_list:
|
|
litellm_args = deployment["litellm_params"]
|
|
try:
|
|
start_time = time.time()
|
|
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
|
|
end_time = time.time()
|
|
response_time = end_time - start_time
|
|
logging.debug(f"response_time: {response_time}")
|
|
healthy_deployments.append((deployment, response_time))
|
|
healthy_deployments.sort(key=lambda x: x[1])
|
|
except Exception as e:
|
|
pass
|
|
return healthy_deployments
|
|
|
|
def weighted_shuffle_by_latency(self, items):
|
|
# Sort the items by latency
|
|
sorted_items = sorted(items, key=lambda x: x[1])
|
|
# Get only the latencies
|
|
latencies = [i[1] for i in sorted_items]
|
|
# Calculate the sum of all latencies
|
|
total_latency = sum(latencies)
|
|
# Calculate the weight for each latency (lower latency = higher weight)
|
|
weights = [total_latency-latency for latency in latencies]
|
|
# Get a weighted random item
|
|
if sum(weights) == 0:
|
|
chosen_item = random.choice(sorted_items)[0]
|
|
else:
|
|
chosen_item = random.choices(sorted_items, weights=weights, k=1)[0][0]
|
|
return chosen_item
|
|
|
|
def set_model_list(self, model_list: list):
|
|
self.model_list = model_list
|
|
self.model_names = [m["model_name"] for m in model_list]
|
|
|
|
def get_model_names(self):
|
|
return self.model_names
|
|
|
|
def print_verbose(self, print_statement):
|
|
if self.set_verbose or litellm.set_verbose:
|
|
print(f"LiteLLM.Router: {print_statement}") # noqa
|
|
|
|
def get_available_deployment(self,
|
|
model: str,
|
|
messages: Optional[List[Dict[str, str]]] = None,
|
|
input: Optional[Union[str, List]] = None):
|
|
"""
|
|
Returns the deployment based on routing strategy
|
|
"""
|
|
## get healthy deployments
|
|
### get all deployments
|
|
### filter out the deployments currently cooling down
|
|
healthy_deployments = [m for m in self.model_list if m["model_name"] == model]
|
|
deployments_to_remove = []
|
|
cooldown_deployments = self._get_cooldown_deployments()
|
|
self.print_verbose(f"cooldown deployments: {cooldown_deployments}")
|
|
### FIND UNHEALTHY DEPLOYMENTS
|
|
for deployment in healthy_deployments:
|
|
deployment_name = deployment["litellm_params"]["model"]
|
|
if deployment_name in cooldown_deployments:
|
|
deployments_to_remove.append(deployment)
|
|
### FILTER OUT UNHEALTHY DEPLOYMENTS
|
|
for deployment in deployments_to_remove:
|
|
healthy_deployments.remove(deployment)
|
|
self.print_verbose(f"healthy deployments: {healthy_deployments}")
|
|
if len(healthy_deployments) == 0:
|
|
raise ValueError("No models available")
|
|
if litellm.model_alias_map and model in litellm.model_alias_map:
|
|
model = litellm.model_alias_map[
|
|
model
|
|
] # update the model to the actual value if an alias has been passed in
|
|
if self.routing_strategy == "least-busy":
|
|
if len(self.healthy_deployments) > 0:
|
|
for item in self.healthy_deployments:
|
|
if item[0]["model_name"] == model: # first one in queue will be the one with the most availability
|
|
return item[0]
|
|
else:
|
|
raise ValueError("No models available.")
|
|
elif self.routing_strategy == "simple-shuffle":
|
|
item = random.choice(healthy_deployments)
|
|
return item or item[0]
|
|
elif self.routing_strategy == "latency-based-routing":
|
|
returned_item = None
|
|
lowest_latency = float('inf')
|
|
### shuffles with priority for lowest latency
|
|
# items_with_latencies = [('A', 10), ('B', 20), ('C', 30), ('D', 40)]
|
|
items_with_latencies = []
|
|
for item in healthy_deployments:
|
|
items_with_latencies.append((item, self.deployment_latency_map[item["litellm_params"]["model"]]))
|
|
returned_item = self.weighted_shuffle_by_latency(items_with_latencies)
|
|
return returned_item
|
|
elif self.routing_strategy == "usage-based-routing":
|
|
return self.get_usage_based_available_deployment(model=model, messages=messages, input=input)
|
|
|
|
raise ValueError("No models available.")
|
|
|