mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-26 11:14:04 +00:00
68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
import time
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import asyncio
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import os
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from openai import AsyncOpenAI, AsyncAzureOpenAI
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import uuid
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import traceback
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from large_text import text
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from dotenv import load_dotenv
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from statistics import mean, median
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litellm_client = AsyncOpenAI(base_url="http://0.0.0.0:4000/", api_key="sk-1234")
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async def litellm_completion():
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try:
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start_time = time.time()
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response = await litellm_client.chat.completions.create(
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model="fake-openai-endpoint",
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messages=[
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{
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"role": "user",
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"content": f"This is a test{uuid.uuid4()}",
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}
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],
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user="my-new-end-user-1",
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)
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end_time = time.time()
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latency = end_time - start_time
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print("response time=", latency)
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return response, latency
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except Exception as e:
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with open("error_log.txt", "a") as error_log:
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error_log.write(f"Error during completion: {str(e)}\n")
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return None, 0
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async def main():
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latencies = []
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for i in range(5):
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start = time.time()
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n = 100 # Number of concurrent tasks
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tasks = [litellm_completion() for _ in range(n)]
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chat_completions = await asyncio.gather(*tasks)
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successful_completions = [c for c, l in chat_completions if c is not None]
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completion_latencies = [l for c, l in chat_completions if c is not None]
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latencies.extend(completion_latencies)
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with open("error_log.txt", "a") as error_log:
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for completion, latency in chat_completions:
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if isinstance(completion, str):
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error_log.write(completion + "\n")
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print(n, time.time() - start, len(successful_completions))
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if latencies:
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average_latency = mean(latencies)
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median_latency = median(latencies)
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print(f"Average Latency per Response: {average_latency} seconds")
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print(f"Median Latency per Response: {median_latency} seconds")
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if __name__ == "__main__":
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open("error_log.txt", "w").close()
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asyncio.run(main())
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