mirror of
https://github.com/BerriAI/litellm.git
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131 lines
4.3 KiB
Python
131 lines
4.3 KiB
Python
import json
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import os
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import sys
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import time
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from datetime import datetime
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from unittest.mock import AsyncMock, patch, MagicMock
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import pytest
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import litellm
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model",
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[
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"bedrock/mistral.mistral-7b-instruct-v0:2",
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"openai/gpt-4o",
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"openai/self_hosted",
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"bedrock/anthropic.claude-3-5-haiku-20241022-v1:0",
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"vertex_ai/gemini-1.0-pro-vision-001",
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],
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)
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async def test_litellm_overhead_non_streaming(model):
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"""
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- Test we can see the litellm overhead and that it is less than 40% of the total request time
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"""
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litellm._turn_on_debug()
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start_time = datetime.now()
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kwargs ={
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"messages": [{"role": "user", "content": "Hello, world!"}],
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"model": model
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}
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#########################################################
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# Specific cases for models
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#########################################################
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if model == "vertex_ai/gemini-1.0-pro-vision-001" or model == "openai/self_hosted":
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kwargs["api_base"] = "https://exampleopenaiendpoint-production.up.railway.app/"
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# warmup call for auth validation on vertex_ai models
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await litellm.acompletion(**kwargs)
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response = await litellm.acompletion(
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**kwargs
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)
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#########################################################
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# End of specific cases for models
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#########################################################
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end_time = datetime.now()
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total_time_ms = (end_time - start_time).total_seconds() * 1000
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print(response)
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print(response._hidden_params)
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litellm_overhead_ms = response._hidden_params["litellm_overhead_time_ms"]
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# calculate percent of overhead caused by litellm
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overhead_percent = litellm_overhead_ms * 100 / total_time_ms
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print("##########################\n")
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print("total_time_ms", total_time_ms)
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print("response litellm_overhead_ms", litellm_overhead_ms)
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print("litellm overhead_percent {}%".format(overhead_percent))
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print("##########################\n")
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assert litellm_overhead_ms > 0
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assert litellm_overhead_ms < 1000
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# latency overhead should be less than total request time
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assert litellm_overhead_ms < (end_time - start_time).total_seconds() * 1000
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# latency overhead should be under 40% of total request time
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assert overhead_percent < 40
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pass
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@pytest.mark.asyncio
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@pytest.mark.parametrize(
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"model",
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[
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"bedrock/mistral.mistral-7b-instruct-v0:2",
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"openai/gpt-4o",
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"bedrock/anthropic.claude-3-5-haiku-20241022-v1:0",
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"openai/self_hosted",
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],
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)
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async def test_litellm_overhead_stream(model):
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litellm._turn_on_debug()
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start_time = datetime.now()
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kwargs ={
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"messages": [{"role": "user", "content": "Hello, world!"}],
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"model": model,
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"stream": True,
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}
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#########################################################
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# Specific cases for models
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#########################################################
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if model == "openai/self_hosted":
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kwargs["api_base"] = "https://exampleopenaiendpoint-production.up.railway.app/"
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# warmup call for auth validation on vertex_ai models
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await litellm.acompletion(**kwargs)
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response = await litellm.acompletion(
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**kwargs
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)
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async for chunk in response:
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print()
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end_time = datetime.now()
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total_time_ms = (end_time - start_time).total_seconds() * 1000
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print(response)
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print(response._hidden_params)
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litellm_overhead_ms = response._hidden_params["litellm_overhead_time_ms"]
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# calculate percent of overhead caused by litellm
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overhead_percent = litellm_overhead_ms * 100 / total_time_ms
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print("##########################\n")
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print("total_time_ms", total_time_ms)
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print("response litellm_overhead_ms", litellm_overhead_ms)
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print("litellm overhead_percent {}%".format(overhead_percent))
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print("##########################\n")
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assert litellm_overhead_ms > 0
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assert litellm_overhead_ms < 1000
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# latency overhead should be less than total request time
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assert litellm_overhead_ms < (end_time - start_time).total_seconds() * 1000
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# latency overhead should be under 40% of total request time
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assert overhead_percent < 40
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pass
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