mirror of
https://github.com/BerriAI/litellm.git
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1ea046cc61
* test: update tests to new deployment model * test: update model name * test: skip cohere rbac issue test * test: update test - replace gpt-4o model
741 lines
22 KiB
Python
741 lines
22 KiB
Python
#### What this tests ####
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# This tests calling router with fallback models
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import asyncio
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import os
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import random
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import sys
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import time
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import traceback
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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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from unittest.mock import AsyncMock, MagicMock, patch
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import httpx
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import openai
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import litellm
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from litellm import Router
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from litellm.integrations.custom_logger import CustomLogger
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from litellm.router_utils.cooldown_handlers import _async_get_cooldown_deployments
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from litellm.types.router import (
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DeploymentTypedDict,
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LiteLLMParamsTypedDict,
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AllowedFailsPolicy,
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)
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@pytest.mark.asyncio
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async def test_cooldown_badrequest_error():
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"""
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Test 1. It SHOULD NOT cooldown a deployment on a BadRequestError
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"""
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router = litellm.Router(
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model_list=[
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "azure/chatgpt-v-3",
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"api_key": os.getenv("AZURE_API_KEY"),
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"api_version": os.getenv("AZURE_API_VERSION"),
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"api_base": os.getenv("AZURE_API_BASE"),
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},
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}
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],
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debug_level="DEBUG",
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set_verbose=True,
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cooldown_time=300,
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num_retries=0,
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allowed_fails=0,
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)
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# Act & Assert
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try:
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response = await router.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "gm"}],
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bad_param=200,
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)
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except Exception:
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pass
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await asyncio.sleep(3) # wait for deployment to get cooled-down
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response = await router.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "gm"}],
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mock_response="hello",
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)
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assert response is not None
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print(response)
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@pytest.mark.asyncio
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async def test_dynamic_cooldowns():
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"""
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Assert kwargs for completion/embedding have 'cooldown_time' as a litellm_param
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"""
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# litellm.set_verbose = True
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tmp_mock = MagicMock()
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litellm.failure_callback = [tmp_mock]
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router = Router(
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model_list=[
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{
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"model_name": "my-fake-model",
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"litellm_params": {
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"model": "openai/gpt-1",
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"api_key": "my-key",
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"mock_response": Exception("this is an error"),
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},
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}
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],
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cooldown_time=60,
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)
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try:
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_ = router.completion(
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model="my-fake-model",
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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cooldown_time=0,
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num_retries=0,
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)
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except Exception:
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pass
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tmp_mock.assert_called_once()
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print(tmp_mock.call_count)
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assert "cooldown_time" in tmp_mock.call_args[0][0]["litellm_params"]
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assert tmp_mock.call_args[0][0]["litellm_params"]["cooldown_time"] == 0
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@pytest.mark.parametrize("num_deployments", [1, 2])
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def test_single_deployment_no_cooldowns(num_deployments):
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"""
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Do not cooldown on single deployment.
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Cooldown on multiple deployments.
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"""
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model_list = []
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for i in range(num_deployments):
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model = DeploymentTypedDict(
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model_name="gpt-3.5-turbo",
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litellm_params=LiteLLMParamsTypedDict(
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model="gpt-3.5-turbo",
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),
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)
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model_list.append(model)
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router = Router(model_list=model_list, num_retries=0)
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with patch.object(
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router.cooldown_cache, "add_deployment_to_cooldown", new=MagicMock()
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) as mock_client:
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try:
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router.completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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mock_response="litellm.RateLimitError",
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)
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except litellm.RateLimitError:
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pass
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if num_deployments == 1:
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mock_client.assert_not_called()
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else:
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mock_client.assert_called_once()
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@pytest.mark.asyncio
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async def test_single_deployment_no_cooldowns_test_prod():
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"""
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Do not cooldown on single deployment.
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"""
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router = Router(
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model_list=[
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "gpt-3.5-turbo",
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},
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},
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{
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"model_name": "gpt-5",
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"litellm_params": {
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"model": "openai/gpt-5",
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},
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},
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{
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"model_name": "gpt-12",
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"litellm_params": {
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"model": "openai/gpt-12",
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},
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},
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],
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num_retries=0,
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)
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with patch.object(
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router.cooldown_cache, "add_deployment_to_cooldown", new=MagicMock()
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) as mock_client:
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try:
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await router.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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mock_response="litellm.RateLimitError",
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)
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except litellm.RateLimitError:
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pass
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await asyncio.sleep(2)
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mock_client.assert_not_called()
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@pytest.mark.asyncio
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async def test_single_deployment_cooldown_with_allowed_fails():
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"""
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When `allowed_fails` is set, use the allowed_fails to determine cooldown for 1 deployment
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"""
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router = Router(
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model_list=[
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "gpt-3.5-turbo",
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},
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},
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{
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"model_name": "gpt-5",
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"litellm_params": {
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"model": "openai/gpt-5",
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},
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},
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{
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"model_name": "gpt-12",
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"litellm_params": {
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"model": "openai/gpt-12",
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},
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},
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],
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allowed_fails=1,
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num_retries=0,
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)
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with patch.object(
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router.cooldown_cache, "add_deployment_to_cooldown", new=MagicMock()
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) as mock_client:
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for _ in range(2):
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try:
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await router.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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timeout=0.0001,
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)
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except litellm.Timeout:
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pass
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await asyncio.sleep(2)
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mock_client.assert_called_once()
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@pytest.mark.asyncio
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async def test_single_deployment_cooldown_with_allowed_fail_policy():
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"""
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When `allowed_fails_policy` is set, use the allowed_fails_policy to determine cooldown for 1 deployment
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"""
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router = Router(
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model_list=[
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "gpt-3.5-turbo",
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},
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},
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{
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"model_name": "gpt-5",
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"litellm_params": {
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"model": "openai/gpt-5",
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},
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},
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{
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"model_name": "gpt-12",
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"litellm_params": {
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"model": "openai/gpt-12",
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},
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},
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],
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allowed_fails_policy=AllowedFailsPolicy(
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TimeoutErrorAllowedFails=1,
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),
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num_retries=0,
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)
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with patch.object(
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router.cooldown_cache, "add_deployment_to_cooldown", new=MagicMock()
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) as mock_client:
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for _ in range(2):
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try:
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await router.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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timeout=0.0001,
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)
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except litellm.Timeout:
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pass
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await asyncio.sleep(2)
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mock_client.assert_called_once()
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@pytest.mark.asyncio
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async def test_single_deployment_no_cooldowns_test_prod_mock_completion_calls():
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"""
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Do not cooldown on single deployment.
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"""
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router = Router(
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model_list=[
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "gpt-3.5-turbo",
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},
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},
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{
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"model_name": "gpt-5",
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"litellm_params": {
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"model": "openai/gpt-5",
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},
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},
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{
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"model_name": "gpt-12",
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"litellm_params": {
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"model": "openai/gpt-12",
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},
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},
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],
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)
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for _ in range(20):
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try:
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await router.acompletion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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mock_response="litellm.RateLimitError",
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)
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except litellm.RateLimitError:
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pass
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cooldown_list = await _async_get_cooldown_deployments(
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litellm_router_instance=router, parent_otel_span=None
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)
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assert len(cooldown_list) == 0
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healthy_deployments, _ = await router._async_get_healthy_deployments(
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model="gpt-3.5-turbo", parent_otel_span=None
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)
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print("healthy_deployments: ", healthy_deployments)
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"""
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E2E - Test router cooldowns
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Test 1: 3 deployments, each deployment fails 25% requests. Assert that no deployments get put into cooldown
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Test 2: 3 deployments, 1- deployment fails 6/10 requests, assert that bad deployment gets put into cooldown
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Test 3: 3 deployments, 1 deployment has a period of 429 errors. Assert it is put into cooldown and other deployments work
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"""
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@pytest.mark.asyncio()
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async def test_high_traffic_cooldowns_all_healthy_deployments():
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"""
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PROD TEST - 3 deployments, each deployment fails 25% requests. Assert that no deployments get put into cooldown
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"""
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router = Router(
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model_list=[
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "gpt-3.5-turbo",
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"api_base": "https://api.openai.com",
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},
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},
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "gpt-3.5-turbo",
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"api_base": "https://api.openai.com-2",
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},
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},
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{
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"model_name": "gpt-3.5-turbo",
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"litellm_params": {
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"model": "gpt-3.5-turbo",
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"api_base": "https://api.openai.com-3",
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},
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},
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],
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set_verbose=True,
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debug_level="DEBUG",
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)
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all_deployment_ids = router.get_model_ids()
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import random
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from collections import defaultdict
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# Create a defaultdict to track successes and failures for each model ID
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model_stats = defaultdict(lambda: {"successes": 0, "failures": 0})
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litellm.set_verbose = True
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for _ in range(100):
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try:
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model_id = random.choice(all_deployment_ids)
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num_successes = model_stats[model_id]["successes"]
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num_failures = model_stats[model_id]["failures"]
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total_requests = num_failures + num_successes
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if total_requests > 0:
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print(
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"num failures= ",
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num_failures,
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"num successes= ",
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num_successes,
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"num_failures/total = ",
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num_failures / total_requests,
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)
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if total_requests == 0:
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mock_response = "hi"
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elif num_failures / total_requests <= 0.25:
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# Randomly decide between fail and succeed
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if random.random() < 0.5:
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mock_response = "hi"
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else:
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mock_response = "litellm.InternalServerError"
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else:
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mock_response = "hi"
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await router.acompletion(
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model=model_id,
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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mock_response=mock_response,
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)
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model_stats[model_id]["successes"] += 1
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await asyncio.sleep(0.0001)
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except litellm.InternalServerError:
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model_stats[model_id]["failures"] += 1
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pass
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except Exception as e:
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print("Failed test model stats=", model_stats)
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raise e
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print("model_stats: ", model_stats)
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cooldown_list = await _async_get_cooldown_deployments(
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litellm_router_instance=router, parent_otel_span=None
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)
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assert len(cooldown_list) == 0
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|
|
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@pytest.mark.asyncio()
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async def test_high_traffic_cooldowns_one_bad_deployment():
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"""
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PROD TEST - 3 deployments, 1- deployment fails 6/10 requests, assert that bad deployment gets put into cooldown
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"""
|
|
|
|
router = Router(
|
|
model_list=[
|
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{
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"model_name": "gpt-3.5-turbo",
|
|
"litellm_params": {
|
|
"model": "gpt-3.5-turbo",
|
|
"api_base": "https://api.openai.com",
|
|
},
|
|
},
|
|
{
|
|
"model_name": "gpt-3.5-turbo",
|
|
"litellm_params": {
|
|
"model": "gpt-3.5-turbo",
|
|
"api_base": "https://api.openai.com-2",
|
|
},
|
|
},
|
|
{
|
|
"model_name": "gpt-3.5-turbo",
|
|
"litellm_params": {
|
|
"model": "gpt-3.5-turbo",
|
|
"api_base": "https://api.openai.com-3",
|
|
},
|
|
},
|
|
],
|
|
set_verbose=True,
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|
debug_level="DEBUG",
|
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)
|
|
|
|
all_deployment_ids = router.get_model_ids()
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|
|
|
import random
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from collections import defaultdict
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|
|
|
# Create a defaultdict to track successes and failures for each model ID
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model_stats = defaultdict(lambda: {"successes": 0, "failures": 0})
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bad_deployment_id = random.choice(all_deployment_ids)
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litellm.set_verbose = True
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for _ in range(100):
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try:
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model_id = random.choice(all_deployment_ids)
|
|
|
|
num_successes = model_stats[model_id]["successes"]
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num_failures = model_stats[model_id]["failures"]
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total_requests = num_failures + num_successes
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if total_requests > 0:
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print(
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"num failures= ",
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num_failures,
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"num successes= ",
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num_successes,
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"num_failures/total = ",
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num_failures / total_requests,
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)
|
|
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|
if total_requests == 0:
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mock_response = "hi"
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elif bad_deployment_id == model_id:
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if num_failures / total_requests <= 0.6:
|
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mock_response = "litellm.InternalServerError"
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|
|
elif num_failures / total_requests <= 0.25:
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# Randomly decide between fail and succeed
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if random.random() < 0.5:
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mock_response = "hi"
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|
else:
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mock_response = "litellm.InternalServerError"
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|
else:
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mock_response = "hi"
|
|
|
|
await router.acompletion(
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model=model_id,
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messages=[{"role": "user", "content": "Hey, how's it going?"}],
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mock_response=mock_response,
|
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)
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model_stats[model_id]["successes"] += 1
|
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|
|
await asyncio.sleep(0.0001)
|
|
except litellm.InternalServerError:
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model_stats[model_id]["failures"] += 1
|
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pass
|
|
except Exception as e:
|
|
print("Failed test model stats=", model_stats)
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|
raise e
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|
print("model_stats: ", model_stats)
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|
|
cooldown_list = await _async_get_cooldown_deployments(
|
|
litellm_router_instance=router, parent_otel_span=None
|
|
)
|
|
assert len(cooldown_list) == 1
|
|
|
|
|
|
@pytest.mark.asyncio()
|
|
async def test_high_traffic_cooldowns_one_rate_limited_deployment():
|
|
"""
|
|
PROD TEST - 3 deployments, 1- deployment fails 6/10 requests, assert that bad deployment gets put into cooldown
|
|
"""
|
|
|
|
router = Router(
|
|
model_list=[
|
|
{
|
|
"model_name": "gpt-3.5-turbo",
|
|
"litellm_params": {
|
|
"model": "gpt-3.5-turbo",
|
|
"api_base": "https://api.openai.com",
|
|
},
|
|
},
|
|
{
|
|
"model_name": "gpt-3.5-turbo",
|
|
"litellm_params": {
|
|
"model": "gpt-3.5-turbo",
|
|
"api_base": "https://api.openai.com-2",
|
|
},
|
|
},
|
|
{
|
|
"model_name": "gpt-3.5-turbo",
|
|
"litellm_params": {
|
|
"model": "gpt-3.5-turbo",
|
|
"api_base": "https://api.openai.com-3",
|
|
},
|
|
},
|
|
],
|
|
set_verbose=True,
|
|
debug_level="DEBUG",
|
|
)
|
|
|
|
all_deployment_ids = router.get_model_ids()
|
|
|
|
import random
|
|
from collections import defaultdict
|
|
|
|
# Create a defaultdict to track successes and failures for each model ID
|
|
model_stats = defaultdict(lambda: {"successes": 0, "failures": 0})
|
|
bad_deployment_id = random.choice(all_deployment_ids)
|
|
litellm.set_verbose = True
|
|
for _ in range(100):
|
|
try:
|
|
model_id = random.choice(all_deployment_ids)
|
|
|
|
num_successes = model_stats[model_id]["successes"]
|
|
num_failures = model_stats[model_id]["failures"]
|
|
total_requests = num_failures + num_successes
|
|
if total_requests > 0:
|
|
print(
|
|
"num failures= ",
|
|
num_failures,
|
|
"num successes= ",
|
|
num_successes,
|
|
"num_failures/total = ",
|
|
num_failures / total_requests,
|
|
)
|
|
|
|
if total_requests == 0:
|
|
mock_response = "hi"
|
|
elif bad_deployment_id == model_id:
|
|
if num_failures / total_requests <= 0.6:
|
|
|
|
mock_response = "litellm.RateLimitError"
|
|
|
|
elif num_failures / total_requests <= 0.25:
|
|
# Randomly decide between fail and succeed
|
|
if random.random() < 0.5:
|
|
mock_response = "hi"
|
|
else:
|
|
mock_response = "litellm.InternalServerError"
|
|
else:
|
|
mock_response = "hi"
|
|
|
|
await router.acompletion(
|
|
model=model_id,
|
|
messages=[{"role": "user", "content": "Hey, how's it going?"}],
|
|
mock_response=mock_response,
|
|
)
|
|
model_stats[model_id]["successes"] += 1
|
|
|
|
await asyncio.sleep(0.0001)
|
|
except litellm.InternalServerError:
|
|
model_stats[model_id]["failures"] += 1
|
|
pass
|
|
except litellm.RateLimitError:
|
|
model_stats[bad_deployment_id]["failures"] += 1
|
|
pass
|
|
except Exception as e:
|
|
print("Failed test model stats=", model_stats)
|
|
raise e
|
|
print("model_stats: ", model_stats)
|
|
|
|
cooldown_list = await _async_get_cooldown_deployments(
|
|
litellm_router_instance=router, parent_otel_span=None
|
|
)
|
|
assert len(cooldown_list) == 1
|
|
|
|
|
|
"""
|
|
Unit tests for router set_cooldowns
|
|
|
|
1. _set_cooldown_deployments() will cooldown a deployment after it fails 50% requests
|
|
"""
|
|
|
|
|
|
def test_router_fallbacks_with_cooldowns_and_model_id():
|
|
router = Router(
|
|
model_list=[
|
|
{
|
|
"model_name": "gpt-3.5-turbo",
|
|
"litellm_params": {"model": "gpt-3.5-turbo", "rpm": 1},
|
|
"model_info": {
|
|
"id": "123",
|
|
},
|
|
}
|
|
],
|
|
routing_strategy="usage-based-routing-v2",
|
|
fallbacks=[{"gpt-3.5-turbo": ["123"]}],
|
|
)
|
|
|
|
## trigger ratelimit
|
|
try:
|
|
router.completion(
|
|
model="gpt-3.5-turbo",
|
|
messages=[{"role": "user", "content": "hi"}],
|
|
mock_response="litellm.RateLimitError",
|
|
)
|
|
except litellm.RateLimitError:
|
|
pass
|
|
|
|
router.completion(
|
|
model="gpt-3.5-turbo",
|
|
messages=[{"role": "user", "content": "hi"}],
|
|
)
|
|
|
|
|
|
@pytest.mark.asyncio()
|
|
async def test_router_fallbacks_with_cooldowns_and_dynamic_credentials():
|
|
"""
|
|
Ensure cooldown on credential 1 does not affect credential 2
|
|
"""
|
|
from litellm.router_utils.cooldown_handlers import _async_get_cooldown_deployments
|
|
|
|
litellm._turn_on_debug()
|
|
router = Router(
|
|
model_list=[
|
|
{
|
|
"model_name": "gpt-3.5-turbo",
|
|
"litellm_params": {"model": "gpt-3.5-turbo", "rpm": 1},
|
|
"model_info": {
|
|
"id": "123",
|
|
},
|
|
}
|
|
]
|
|
)
|
|
|
|
## trigger ratelimit
|
|
try:
|
|
await router.acompletion(
|
|
model="gpt-3.5-turbo",
|
|
messages=[{"role": "user", "content": "hi"}],
|
|
api_key="my-bad-key-1",
|
|
mock_response="litellm.RateLimitError",
|
|
)
|
|
pytest.fail("Expected RateLimitError")
|
|
except litellm.RateLimitError:
|
|
pass
|
|
|
|
await asyncio.sleep(1)
|
|
|
|
cooldown_list = await _async_get_cooldown_deployments(
|
|
litellm_router_instance=router, parent_otel_span=None
|
|
)
|
|
print("cooldown_list: ", cooldown_list)
|
|
assert len(cooldown_list) == 1
|
|
|
|
await router.acompletion(
|
|
model="gpt-3.5-turbo",
|
|
api_key=os.getenv("OPENAI_API_KEY"),
|
|
messages=[{"role": "user", "content": "hi"}],
|
|
)
|