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Load Test LiteLLM

How to run a locust load test on LiteLLM Proxy​

  1. Add fake-openai-endpoint to your proxy config.yaml and start your litellm proxy litellm provides a free hosted fake-openai-endpoint you can load test against
model_list:
  - model_name: fake-openai-endpoint
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/

  1. pip install locust

  2. Create a file called locustfile.py on your local machine. Copy the contents from the litellm load test located here

  3. Start locust Run locust in the same directory as your locustfile.py from step 2

    locust

    Output on terminal 

    [2024-03-15 07:19:58,893] Starting web interface at http://0.0.0.0:8089
    [2024-03-15 07:19:58,898] Starting Locust 2.24.0

  4. Run Load test on locust

    Head to the locust UI on http://0.0.0.0:8089

    Set Users=100, Ramp Up Users=10, Host=Base URL of your LiteLLM Proxy

  5. Expected Results

    Expect to see the following response times for /health/readiness Median → /health/readiness is 150ms

    Avg → /health/readiness is 219ms

Load Test LiteLLM Proxy - 1500+ req/s​

1500+ concurrent requests/s​

LiteLLM proxy has been load tested to handle 1500+ concurrent req/s

import time, asyncio
from openai import AsyncOpenAI, AsyncAzureOpenAI
import uuid
import traceback

# base_url - litellm proxy endpoint
# api_key - litellm proxy api-key, is created proxy with auth
litellm_client = AsyncOpenAI(base_url="http://0.0.0.0:4000", api_key="sk-1234")


async def litellm_completion():
    # Your existing code for litellm_completion goes here
    try:
        response = await litellm_client.chat.completions.create(
            model="azure-gpt-3.5",
            messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
        )
        print(response)
        return response

    except Exception as e:
        # If there's an exception, log the error message
        with open("error_log.txt", "a") as error_log:
            error_log.write(f"Error during completion: {str(e)}\n")
        pass


async def main():
    for i in range(1):
        start = time.time()
        n = 1500  # Number of concurrent tasks
        tasks = [litellm_completion() for _ in range(n)]

        chat_completions = await asyncio.gather(*tasks)

        successful_completions = [c for c in chat_completions if c is not None]

        # Write errors to error_log.txt
        with open("error_log.txt", "a") as error_log:
            for completion in chat_completions:
                if isinstance(completion, str):
                    error_log.write(completion + "\n")

        print(n, time.time() - start, len(successful_completions))
        time.sleep(10)


if __name__ == "__main__":
    # Blank out contents of error_log.txt
    open("error_log.txt", "w").close()

    asyncio.run(main())

Throughput - 30% Increase​

LiteLLM proxy + Load Balancer gives 30% increase in throughput compared to Raw OpenAI API

Latency Added - 0.00325 seconds​

LiteLLM proxy adds 0.00325 seconds latency as compared to using the Raw OpenAI API

Testing LiteLLM Proxy with Locust​

  • 1 LiteLLM container can handle ~140 requests/second with 0.4 failures

Load Test LiteLLM SDK vs OpenAI​

Here is a script to load test LiteLLM vs OpenAI 

from openai import AsyncOpenAI, AsyncAzureOpenAI
import random, uuid
import time, asyncio, litellm
# import logging
# logging.basicConfig(level=logging.DEBUG)
#### LITELLM PROXY #### 
litellm_client = AsyncOpenAI(
    api_key="sk-1234", # [CHANGE THIS]
    base_url="http://0.0.0.0:4000"
)

#### AZURE OPENAI CLIENT #### 
client = AsyncAzureOpenAI(
    api_key="my-api-key", # [CHANGE THIS]
    azure_endpoint="my-api-base", # [CHANGE THIS]
    api_version="2023-07-01-preview" 
)


#### LITELLM ROUTER #### 
model_list = [
  {
    "model_name": "azure-canada",
    "litellm_params": {
      "model": "azure/my-azure-deployment-name", # [CHANGE THIS]
      "api_key": "my-api-key", # [CHANGE THIS]
      "api_base": "my-api-base", # [CHANGE THIS]
      "api_version": "2023-07-01-preview"
    }
  }
]

router = litellm.Router(model_list=model_list)

async def openai_completion():
  try:
    response = await client.chat.completions.create(
              model="gpt-35-turbo",
              messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
              stream=True
          )
    return response
  except Exception as e:
    print(e)
    return None
  

async def router_completion():
  try:
    response = await router.acompletion(
              model="azure-canada", # [CHANGE THIS]
              messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
              stream=True
          )
    return response
  except Exception as e:
    print(e)
    return None

async def proxy_completion_non_streaming():
  try:
    response = await litellm_client.chat.completions.create(
              model="sagemaker-models", # [CHANGE THIS] (if you call it something else on your proxy)
              messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
          )
    return response
  except Exception as e:
    print(e)
    return None

async def loadtest_fn():
    start = time.time()
    n = 500  # Number of concurrent tasks
    tasks = [proxy_completion_non_streaming() for _ in range(n)]
    chat_completions = await asyncio.gather(*tasks)
    successful_completions = [c for c in chat_completions if c is not None]
    print(n, time.time() - start, len(successful_completions))

# Run the event loop to execute the async function
asyncio.run(loadtest_fn())

Multi-Instance TPM/RPM Load Test (Router)​

Test if your defined tpm/rpm limits are respected across multiple instances of the Router object.

In our test:

  • Max RPM per deployment is = 100 requests per minute
  • Max Throughput / min on router = 200 requests per minute (2 deployments)
  • Load we'll send through router = 600 requests per minute
info

If you don't want to call a real LLM API endpoint, you can setup a fake openai server. See code

Code​

Let's hit the router with 600 requests per minute.

Copy this script 👇. Save it as test_loadtest_router.py AND run it with python3 test_loadtest_router.py

from litellm import Router 
import litellm
litellm.suppress_debug_info = True
litellm.set_verbose = False
import logging
logging.basicConfig(level=logging.CRITICAL)
import os, random, uuid, time, asyncio

# Model list for OpenAI and Anthropic models
model_list = [
    {
        "model_name": "fake-openai-endpoint",
        "litellm_params": {
            "model": "gpt-3.5-turbo",
            "api_key": "my-fake-key",
            "api_base": "http://0.0.0.0:8080",
            "rpm": 100
        },
    },
    {
        "model_name": "fake-openai-endpoint",
        "litellm_params": {
            "model": "gpt-3.5-turbo",
            "api_key": "my-fake-key",
            "api_base": "http://0.0.0.0:8081",
            "rpm": 100
        },
    },
]

router_1 = Router(model_list=model_list, num_retries=0, enable_pre_call_checks=True, routing_strategy="usage-based-routing-v2", redis_host=os.getenv("REDIS_HOST"), redis_port=os.getenv("REDIS_PORT"), redis_password=os.getenv("REDIS_PASSWORD"))
router_2 = Router(model_list=model_list, num_retries=0, routing_strategy="usage-based-routing-v2", enable_pre_call_checks=True, redis_host=os.getenv("REDIS_HOST"), redis_port=os.getenv("REDIS_PORT"), redis_password=os.getenv("REDIS_PASSWORD"))



async def router_completion_non_streaming():
  try:
    client: Router = random.sample([router_1, router_2], 1)[0] # randomly pick b/w clients
    # print(f"client={client}")
    response = await client.acompletion(
              model="fake-openai-endpoint", # [CHANGE THIS] (if you call it something else on your proxy)
              messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
          )
    return response
  except Exception as e:
    # print(e)
    return None
  
async def loadtest_fn():
    start = time.time()
    n = 600  # Number of concurrent tasks
    tasks = [router_completion_non_streaming() for _ in range(n)]
    chat_completions = await asyncio.gather(*tasks)
    successful_completions = [c for c in chat_completions if c is not None]
    print(n, time.time() - start, len(successful_completions))

def get_utc_datetime():
    import datetime as dt
    from datetime import datetime

    if hasattr(dt, "UTC"):
        return datetime.now(dt.UTC)  # type: ignore
    else:
        return datetime.utcnow()  # type: ignore


# Run the event loop to execute the async function
async def parent_fn():
  for _ in range(10):
    dt = get_utc_datetime()
    current_minute = dt.strftime("%H-%M")
    print(f"triggered new batch - {current_minute}")
    await loadtest_fn()
    await asyncio.sleep(10)

asyncio.run(parent_fn())

Multi-Instance TPM/RPM Load Test (Proxy)​

Test if your defined tpm/rpm limits are respected across multiple instances.

The quickest way to do this is by testing the proxy. The proxy uses the router under the hood, so if you're using either of them, this test should work for you.

In our test:

  • Max RPM per deployment is = 100 requests per minute
  • Max Throughput / min on proxy = 200 requests per minute (2 deployments)
  • Load we'll send to proxy = 600 requests per minute

So we'll send 600 requests per minute, but expect only 200 requests per minute to succeed.

info

If you don't want to call a real LLM API endpoint, you can setup a fake openai server. See code

1. Setup config​

model_list:
- litellm_params:
    api_base: http://0.0.0.0:8080
    api_key: my-fake-key
    model: openai/my-fake-model
    rpm: 100
  model_name: fake-openai-endpoint
- litellm_params:
    api_base: http://0.0.0.0:8081
    api_key: my-fake-key
    model: openai/my-fake-model-2
    rpm: 100
  model_name: fake-openai-endpoint
router_settings:
  num_retries: 0
  enable_pre_call_checks: true
  redis_host: os.environ/REDIS_HOST ## 👈 IMPORTANT! Setup the proxy w/ redis
  redis_password: os.environ/REDIS_PASSWORD
  redis_port: os.environ/REDIS_PORT
  routing_strategy: usage-based-routing-v2

2. Start proxy 2 instances​

Instance 1

litellm --config /path/to/config.yaml --port 4000

## RUNNING on http://0.0.0.0:4000

Instance 2

litellm --config /path/to/config.yaml --port 4001

## RUNNING on http://0.0.0.0:4001

3. Run Test​

Let's hit the proxy with 600 requests per minute.

Copy this script 👇. Save it as test_loadtest_proxy.py AND run it with python3 test_loadtest_proxy.py

from openai import AsyncOpenAI, AsyncAzureOpenAI
import random, uuid
import time, asyncio, litellm
# import logging
# logging.basicConfig(level=logging.DEBUG)
#### LITELLM PROXY #### 
litellm_client = AsyncOpenAI(
    api_key="sk-1234", # [CHANGE THIS]
    base_url="http://0.0.0.0:4000"
)
litellm_client_2 = AsyncOpenAI(
    api_key="sk-1234", # [CHANGE THIS]
    base_url="http://0.0.0.0:4001"
)

async def proxy_completion_non_streaming():
  try:
    client = random.sample([litellm_client, litellm_client_2], 1)[0] # randomly pick b/w clients
    # print(f"client={client}")
    response = await client.chat.completions.create(
              model="fake-openai-endpoint", # [CHANGE THIS] (if you call it something else on your proxy)
              messages=[{"role": "user", "content": f"This is a test: {uuid.uuid4()}"}],
          )
    return response
  except Exception as e:
    # print(e)
    return None
  
async def loadtest_fn():
    start = time.time()
    n = 600  # Number of concurrent tasks
    tasks = [proxy_completion_non_streaming() for _ in range(n)]
    chat_completions = await asyncio.gather(*tasks)
    successful_completions = [c for c in chat_completions if c is not None]
    print(n, time.time() - start, len(successful_completions))

def get_utc_datetime():
    import datetime as dt
    from datetime import datetime

    if hasattr(dt, "UTC"):
        return datetime.now(dt.UTC)  # type: ignore
    else:
        return datetime.utcnow()  # type: ignore


# Run the event loop to execute the async function
async def parent_fn():
  for _ in range(10):
    dt = get_utc_datetime()
    current_minute = dt.strftime("%H-%M")
    print(f"triggered new batch - {current_minute}")
    await loadtest_fn()
    await asyncio.sleep(10)

asyncio.run(parent_fn())

Extra - Setup Fake OpenAI Server​

Let's setup a fake openai server with a RPM limit of 100.

Let's call our file fake_openai_server.py. 

# import sys, os
# sys.path.insert(
#     0, os.path.abspath("../")
# )  # Adds the parent directory to the system path
from fastapi import FastAPI, Request, status, HTTPException, Depends
from fastapi.responses import StreamingResponse
from fastapi.security import OAuth2PasswordBearer
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from fastapi import FastAPI, Request, HTTPException, UploadFile, File
import httpx, os, json
from openai import AsyncOpenAI
from typing import Optional
from slowapi import Limiter
from slowapi.util import get_remote_address
from slowapi.errors import RateLimitExceeded
from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import PlainTextResponse


class ProxyException(Exception):
    # NOTE: DO NOT MODIFY THIS
    # This is used to map exactly to OPENAI Exceptions
    def __init__(
        self,
        message: str,
        type: str,
        param: Optional[str],
        code: Optional[int],
    ):
        self.message = message
        self.type = type
        self.param = param
        self.code = code

    def to_dict(self) -> dict:
        """Converts the ProxyException instance to a dictionary."""
        return {
            "message": self.message,
            "type": self.type,
            "param": self.param,
            "code": self.code,
        }


limiter = Limiter(key_func=get_remote_address)
app = FastAPI()
app.state.limiter = limiter

@app.exception_handler(RateLimitExceeded)
async def _rate_limit_exceeded_handler(request: Request, exc: RateLimitExceeded):
    return JSONResponse(status_code=429,
                        content={"detail": "Rate Limited!"})

app.add_exception_handler(RateLimitExceeded, _rate_limit_exceeded_handler)

app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# for completion
@app.post("/chat/completions")
@app.post("/v1/chat/completions")
@limiter.limit("100/minute")
async def completion(request: Request):
    # raise HTTPException(status_code=429, detail="Rate Limited!")
    return {
        "id": "chatcmpl-123",
        "object": "chat.completion",
        "created": 1677652288,
        "model": None,
        "system_fingerprint": "fp_44709d6fcb",
        "choices": [{
            "index": 0,
            "message": {
            "role": "assistant",
            "content": "\n\nHello there, how may I assist you today?",
            },
            "logprobs": None,
            "finish_reason": "stop"
        }],
        "usage": {
            "prompt_tokens": 9,
            "completion_tokens": 12,
            "total_tokens": 21
        }
    }

if __name__ == "__main__":
    import socket
    import uvicorn
    port = 8080
    while True:
        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        result = sock.connect_ex(('0.0.0.0', port))
        if result != 0:
            print(f"Port {port} is available, starting server...")
            break
        else:
            port += 1

    uvicorn.run(app, host="0.0.0.0", port=port)
python3 fake_openai_server.py