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fix(router.py): add support for cooldowns with redis

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Krrish Dholakia 2023-11-22 19:54:15 -08:00
parent 52e2ac0106
commit 497419a766
3 changed files with 161 additions and 121 deletions
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8
litellm/caching.py
View file

@ -9,7 +9,7 @@
import litellm import litellm
import time, logging import time, logging
import json, traceback import json, traceback, ast
def get_prompt(*args, **kwargs): def get_prompt(*args, **kwargs):
# make this safe checks, it should not throw any exceptions # make this safe checks, it should not throw any exceptions
@ -53,7 +53,11 @@ class RedisCache(BaseCache):
if cached_response != None: if cached_response != None:
# cached_response is in `b{} convert it to ModelResponse # cached_response is in `b{} convert it to ModelResponse
cached_response = cached_response.decode("utf-8") # Convert bytes to string cached_response = cached_response.decode("utf-8") # Convert bytes to string
try:
cached_response = json.loads(cached_response) # Convert string to dictionary cached_response = json.loads(cached_response) # Convert string to dictionary
except:
cached_response = ast.literal_eval(cached_response)
if isinstance(cached_response, dict):
cached_response['cache'] = True # set cache-hit flag to True cached_response['cache'] = True # set cache-hit flag to True
return cached_response return cached_response
except Exception as e: except Exception as e:
@ -224,5 +228,5 @@ class Cache:
if isinstance(result, litellm.ModelResponse): if isinstance(result, litellm.ModelResponse):
result = result.model_dump_json() result = result.model_dump_json()
self.cache.set_cache(cache_key, result, **kwargs) self.cache.set_cache(cache_key, result, **kwargs)
except: except Exception as e:
pass pass

102
litellm/router.py
View file

@ -83,14 +83,14 @@ class Router:
if cache_responses: if cache_responses:
litellm.cache = litellm.Cache(**cache_config) # use Redis for caching completion requests litellm.cache = litellm.Cache(**cache_config) # use Redis for caching completion requests
self.cache_responses = cache_responses self.cache_responses = cache_responses
self.cache = litellm.Cache(cache_config) # use Redis for tracking load balancing self.cache = litellm.Cache(**cache_config) # use Redis for tracking load balancing
## USAGE TRACKING ## ## USAGE TRACKING ##
if type(litellm.success_callback) == list: if isinstance(litellm.success_callback, list):
litellm.success_callback.append(self.deployment_callback) litellm.success_callback.append(self.deployment_callback)
else: else:
litellm.success_callback = [self.deployment_callback] litellm.success_callback = [self.deployment_callback]
if type(litellm.failure_callback) == list: if isinstance(litellm.failure_callback, list):
litellm.failure_callback.append(self.deployment_callback_on_failure) litellm.failure_callback.append(self.deployment_callback_on_failure)
else: else:
litellm.failure_callback = [self.deployment_callback_on_failure] litellm.failure_callback = [self.deployment_callback_on_failure]
@ -169,13 +169,11 @@ class Router:
current_time = time.time() current_time = time.time()
iter = 0 iter = 0
deployments_to_remove = [] deployments_to_remove = []
cooldown_deployments = self._get_cooldown_deployments()
### FIND UNHEALTHY DEPLOYMENTS ### FIND UNHEALTHY DEPLOYMENTS
for deployment in healthy_deployments: for deployment in healthy_deployments:
deployment_name = deployment["litellm_params"]["model"] deployment_name = deployment["litellm_params"]["model"]
if deployment_name in self.cooldown_deployments: if deployment_name in cooldown_deployments:
if current_time >= self.cooldown_deployments[deployment_name] + 60:
self.cooldown_deployments.pop(deployment_name)
else:
deployments_to_remove.append(deployment) deployments_to_remove.append(deployment)
iter += 1 iter += 1
### FILTER OUT UNHEALTHY DEPLOYMENTS ### FILTER OUT UNHEALTHY DEPLOYMENTS
@ -245,36 +243,31 @@ class Router:
raise e raise e
def function_with_retries(self, *args, **kwargs): def function_with_retries(self, *args, **kwargs):
try: # we'll backoff exponentially with each retry
import tenacity backoff_factor = 1
except Exception as e:
raise Exception(f"tenacity import failed please run `pip install tenacity`. Error{e}")
retry_info = {"attempts": 0, "final_result": None}
def after_callback(retry_state):
retry_info["attempts"] = retry_state.attempt_number
retry_info["final_result"] = retry_state.outcome.result()
if 'model' not in kwargs or 'messages' not in kwargs:
raise ValueError("'model' and 'messages' must be included as keyword arguments")
try:
original_exception = kwargs.pop("original_exception") original_exception = kwargs.pop("original_exception")
original_function = kwargs.pop("original_function") original_function = kwargs.pop("original_function")
if isinstance(original_exception, openai.RateLimitError): for current_attempt in range(self.num_retries):
retryer = tenacity.Retrying(wait=tenacity.wait_exponential(multiplier=1, max=10), self.num_retries -= 1 # decrement the number of retries
stop=tenacity.stop_after_attempt(self.num_retries), try:
reraise=True, # if the function call is successful, no exception will be raised and we'll break out of the loop
after=after_callback) response = original_function(*args, **kwargs)
elif isinstance(original_exception, openai.APIError): return response
retryer = tenacity.Retrying(stop=tenacity.stop_after_attempt(self.num_retries),
reraise=True, except openai.RateLimitError as e:
after=after_callback) # 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
except openai.APIError as e:
# on APIError we immediately retry without any wait, change this if necessary
pass
return retryer(original_function, *args, **kwargs)
except Exception as e: except Exception as e:
raise Exception(f"Error in function_with_retries: {e}\n\nRetry Info: {retry_info}") # for any other exception types, don't retry
raise e
### COMPLETION + EMBEDDING FUNCTIONS ### COMPLETION + EMBEDDING FUNCTIONS
@ -422,7 +415,48 @@ class Router:
custom_llm_provider = kwargs.get("litellm_params", {}).get('custom_llm_provider', None) # i.e. azure custom_llm_provider = kwargs.get("litellm_params", {}).get('custom_llm_provider', None) # i.e. azure
if custom_llm_provider: if custom_llm_provider:
model_name = f"{custom_llm_provider}/{model_name}" model_name = f"{custom_llm_provider}/{model_name}"
self.cooldown_deployments[model_name] = time.time() # put deployment in cooldown mode
self._set_cooldown_deployments(model_name)
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(cache_key=cooldown_key)
# update value
try:
if deployment in cached_value:
pass
else:
cached_value = cached_value + [deployment]
# save updated value
self.cache.add_cache(result=cached_value, cache_key=cooldown_key, ttl=60)
except:
cached_value = [deployment]
# save updated value
self.cache.add_cache(result=cached_value, cache_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(cache_key=cooldown_key) or []
return cooldown_models
def get_usage_based_available_deployment(self, def get_usage_based_available_deployment(self,
model: str, model: str,

108
litellm/tests/test_router.py
View file

@ -16,75 +16,78 @@ from dotenv import load_dotenv
load_dotenv() load_dotenv()
# def test_multiple_deployments(): def test_multiple_deployments():
# import concurrent, time import concurrent, time
# # litellm.set_verbose=True # litellm.set_verbose=True
# futures = {} futures = {}
# model_list = [{ # list of model deployments model_list = [{ # list of model deployments
# "model_name": "gpt-3.5-turbo", # openai model name "model_name": "gpt-3.5-turbo", # openai model name
# "litellm_params": { # params for litellm completion/embedding call "litellm_params": { # params for litellm completion/embedding call
# "model": "azure/chatgpt-v-2", "model": "azure/chatgpt-v-2",
# "api_key": os.getenv("AZURE_API_KEY"), "api_key": "bad-key",
# "api_version": os.getenv("AZURE_API_VERSION"), "api_version": os.getenv("AZURE_API_VERSION"),
# "api_base": os.getenv("AZURE_API_BASE") "api_base": os.getenv("AZURE_API_BASE")
# }, },
# "tpm": 240000, "tpm": 240000,
# "rpm": 1800 "rpm": 1800
# }, { },
# {
# "model_name": "gpt-3.5-turbo", # openai model name # "model_name": "gpt-3.5-turbo", # openai model name
# "litellm_params": { # params for litellm completion/embedding call # "litellm_params": { # params for litellm completion/embedding call
# "model": "azure/chatgpt-functioncalling", # "model": "azure/chatgpt-functioncalling",
# "api_key": os.getenv("AZURE_API_KEY"), # "api_key": "bad-key",
# "api_version": os.getenv("AZURE_API_VERSION"), # "api_version": os.getenv("AZURE_API_VERSION"),
# "api_base": os.getenv("AZURE_API_BASE") # "api_base": os.getenv("AZURE_API_BASE")
# }, # },
# "tpm": 240000, # "tpm": 240000,
# "rpm": 1800 # "rpm": 1800
# }, {
# "model_name": "gpt-3.5-turbo", # openai model name
# "litellm_params": { # params for litellm completion/embedding call
# "model": "gpt-3.5-turbo",
# "api_key": os.getenv("OPENAI_API_KEY"),
# }, # },
# "tpm": 1000000, {
# "rpm": 9000 "model_name": "gpt-3.5-turbo", # openai model name
# }] "litellm_params": { # params for litellm completion/embedding call
"model": "gpt-3.5-turbo",
"api_key": os.getenv("OPENAI_API_KEY"),
},
"tpm": 1000000,
"rpm": 9000
}
]
# router = Router(model_list=model_list, router = Router(model_list=model_list,
# redis_host=os.getenv("REDIS_HOST"), redis_host=os.getenv("REDIS_HOST"),
# redis_password=os.getenv("REDIS_PASSWORD"), redis_password=os.getenv("REDIS_PASSWORD"),
# redis_port=int(os.getenv("REDIS_PORT")), redis_port=int(os.getenv("REDIS_PORT")),
# routing_strategy="simple-shuffle", routing_strategy="simple-shuffle",
# num_retries=3) # type: ignore num_retries=1) # type: ignore
# # router = Router(model_list=model_list, redis_host=os.getenv("REDIS_HOST"), redis_password=os.getenv("REDIS_PASSWORD"), redis_port=int(os.getenv("REDIS_PORT"))) # type: ignore # router = Router(model_list=model_list, redis_host=os.getenv("REDIS_HOST"), redis_password=os.getenv("REDIS_PASSWORD"), redis_port=int(os.getenv("REDIS_PORT"))) # type: ignore
# kwargs = { kwargs = {
# "model": "gpt-3.5-turbo", "model": "gpt-3.5-turbo",
# "messages": [{"role": "user", "content": """Context: "messages": [{"role": "user", "content": """Context:
# In the historical era of Ancient Greece, a multitude of significant individuals lived, contributing immensely to various disciplines like science, politics, philosophy, and literature. For instance, Socrates, a renowned philosopher, primarily focused on ethics. His notable method, the Socratic Method, involved acknowledging one's own ignorance to stimulate critical thinking and illuminate ideas. His student, Plato, another prominent figure, founded the Academy in Athens. He proposed theories on justice, beauty, and equality, and also introduced the theory of forms, which is pivotal to understanding his philosophical insights. Another student of Socrates, Xenophon, distinguished himself more in the domain of history and military affairs. In the historical era of Ancient Greece, a multitude of significant individuals lived, contributing immensely to various disciplines like science, politics, philosophy, and literature. For instance, Socrates, a renowned philosopher, primarily focused on ethics. His notable method, the Socratic Method, involved acknowledging one's own ignorance to stimulate critical thinking and illuminate ideas. His student, Plato, another prominent figure, founded the Academy in Athens. He proposed theories on justice, beauty, and equality, and also introduced the theory of forms, which is pivotal to understanding his philosophical insights. Another student of Socrates, Xenophon, distinguished himself more in the domain of history and military affairs.
# Aristotle, who studied under Plato, led an equally remarkable life. His extensive works have been influential across various domains, including science, logic, metaphysics, ethics, and politics. Perhaps most notably, a substantial portion of the Western intellectual tradition traces back to his writings. He later tutored Alexander the Great who went on to create one of the most vast empires in the world. Aristotle, who studied under Plato, led an equally remarkable life. His extensive works have been influential across various domains, including science, logic, metaphysics, ethics, and politics. Perhaps most notably, a substantial portion of the Western intellectual tradition traces back to his writings. He later tutored Alexander the Great who went on to create one of the most vast empires in the world.
# In the domain of mathematics, Pythagoras and Euclid made significant contributions. Pythagoras is best known for the Pythagorean theorem, a fundamental principle in geometry, while Euclid, often regarded as the father of geometry, wrote "The Elements", a collection of definitions, axioms, theorems, and proofs. In the domain of mathematics, Pythagoras and Euclid made significant contributions. Pythagoras is best known for the Pythagorean theorem, a fundamental principle in geometry, while Euclid, often regarded as the father of geometry, wrote "The Elements", a collection of definitions, axioms, theorems, and proofs.
# Apart from these luminaries, the period also saw a number of influential political figures. Pericles, a prominent and influential Greek statesman, orator, and general of Athens during the Golden Age, specifically between the Persian and Peloponnesian wars, played a significant role in developing the Athenian democracy. Apart from these luminaries, the period also saw a number of influential political figures. Pericles, a prominent and influential Greek statesman, orator, and general of Athens during the Golden Age, specifically between the Persian and Peloponnesian wars, played a significant role in developing the Athenian democracy.
# The Ancient Greek era also witnessed extraordinary advancements in arts and literature. Homer, credited with the creation of the epic poems 'The Iliad' and 'The Odyssey,' is considered one of the greatest poets in history. The tragedies of Sophocles, Aeschylus, and Euripides left an indelible mark on the field of drama, and the comedies of Aristophanes remain influential even today. The Ancient Greek era also witnessed extraordinary advancements in arts and literature. Homer, credited with the creation of the epic poems 'The Iliad' and 'The Odyssey,' is considered one of the greatest poets in history. The tragedies of Sophocles, Aeschylus, and Euripides left an indelible mark on the field of drama, and the comedies of Aristophanes remain influential even today.
# --- ---
# Question: Question:
# Who among the mentioned figures from Ancient Greece contributed to the domain of mathematics and what are their significant contributions?"""}], Who among the mentioned figures from Ancient Greece contributed to the domain of mathematics and what are their significant contributions?"""}],
# } }
# results = [] results = []
# # for _ in range(10): for _ in range(10):
# # print(f"starting!!!") print(f"starting!!!")
# # response = router.completion(**kwargs) response = router.completion(**kwargs)
# # results.append(response) results.append(response)
# # print(len(results)) # print(len(results))
# with ThreadPoolExecutor(max_workers=100) as executor: # with ThreadPoolExecutor(max_workers=100) as executor:
# start_time = time.time() # start_time = time.time()
@ -94,7 +97,7 @@ load_dotenv()
# # Retrieve the results from the futures # # Retrieve the results from the futures
# while futures: # while futures:
# done, not_done = concurrent.futures.wait(futures, return_when=concurrent.futures.FIRST_COMPLETED) # done, not_done = concurrent.futures.wait(futures, timeout=10, return_when=concurrent.futures.FIRST_COMPLETED)
# for future in done: # for future in done:
# try: # try:
# result = future.result() # result = future.result()
@ -108,11 +111,10 @@ load_dotenv()
# end_time = time.time() # end_time = time.time()
# print(f"ELAPSED TIME: {end_time-start_time}") # print(f"ELAPSED TIME: {end_time-start_time}")
# print(f"results: {results}") # Check results
# # Check results
# test_multiple_deployments() test_multiple_deployments()
### FUNCTION CALLING ### FUNCTION CALLING
def test_function_calling(): def test_function_calling():