Added LiteLLM to the stack
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374
Development/litellm/tests/local_testing/test_get_model_info.py
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374
Development/litellm/tests/local_testing/test_get_model_info.py
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# What is this?
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## Unit testing for the 'get_model_info()' function
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import os
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import sys
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import traceback
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import json
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from typing import List, Dict, Any
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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 pytest
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import litellm
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from litellm import get_model_info
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from unittest.mock import AsyncMock, MagicMock, patch
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def test_get_model_info_simple_model_name():
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"""
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tests if model name given, and model exists in model info - the object is returned
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"""
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model = "claude-3-opus-20240229"
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litellm.get_model_info(model)
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def test_get_model_info_custom_llm_with_model_name():
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"""
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Tests if {custom_llm_provider}/{model_name} name given, and model exists in model info, the object is returned
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"""
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model = "anthropic/claude-3-opus-20240229"
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litellm.get_model_info(model)
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def test_get_model_info_custom_llm_with_same_name_vllm(monkeypatch):
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"""
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Tests if {custom_llm_provider}/{model_name} name given, and model exists in model info, the object is returned
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"""
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model = "command-r-plus"
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provider = "openai" # vllm is openai-compatible
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litellm.register_model(
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{
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"openai/command-r-plus": {
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"input_cost_per_token": 0.0,
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"output_cost_per_token": 0.0,
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},
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}
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)
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model_info = litellm.get_model_info(model, custom_llm_provider=provider)
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print("model_info", model_info)
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assert model_info["input_cost_per_token"] == 0.0
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def test_get_model_info_shows_correct_supports_vision():
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info = litellm.get_model_info("gemini/gemini-1.5-flash")
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print("info", info)
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assert info["supports_vision"] is True
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def test_get_model_info_shows_assistant_prefill():
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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info = litellm.get_model_info("deepseek/deepseek-chat")
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print("info", info)
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assert info.get("supports_assistant_prefill") is True
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def test_get_model_info_shows_supports_prompt_caching():
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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info = litellm.get_model_info("deepseek/deepseek-chat")
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print("info", info)
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assert info.get("supports_prompt_caching") is True
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def test_get_model_info_finetuned_models():
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info = litellm.get_model_info("ft:gpt-3.5-turbo:my-org:custom_suffix:id")
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print("info", info)
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assert info["input_cost_per_token"] == 0.000003
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def test_get_model_info_gemini_pro():
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info = litellm.get_model_info("gemini-1.5-pro-002")
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print("info", info)
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assert info["key"] == "gemini-1.5-pro-002"
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def test_get_model_info_ollama_chat():
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from litellm.llms.ollama.completion.transformation import OllamaConfig
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with patch.object(
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litellm.module_level_client,
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"post",
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return_value=MagicMock(
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json=lambda: {
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"model_info": {"llama.context_length": 32768},
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"template": "tools",
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}
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),
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) as mock_client:
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info = OllamaConfig().get_model_info("unknown-model")
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assert info["supports_function_calling"] is True
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info = get_model_info("ollama/unknown-model")
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print("info", info)
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assert info["supports_function_calling"] is True
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mock_client.assert_called()
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print(mock_client.call_args.kwargs)
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assert mock_client.call_args.kwargs["json"]["name"] == "unknown-model"
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def test_get_model_info_bedrock_region():
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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args = {
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"model": "us.anthropic.claude-3-5-sonnet-20241022-v2:0",
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"custom_llm_provider": "bedrock",
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}
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litellm.model_cost.pop("us.anthropic.claude-3-5-sonnet-20241022-v2:0", None)
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info = litellm.get_model_info(**args)
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print("info", info)
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assert info["key"] == "anthropic.claude-3-5-sonnet-20241022-v2:0"
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assert info["litellm_provider"] == "bedrock"
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@pytest.mark.parametrize(
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"model",
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[
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"ft:gpt-3.5-turbo:my-org:custom_suffix:id",
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"ft:gpt-4-0613:my-org:custom_suffix:id",
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"ft:davinci-002:my-org:custom_suffix:id",
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"ft:gpt-4-0613:my-org:custom_suffix:id",
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"ft:babbage-002:my-org:custom_suffix:id",
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"gpt-35-turbo",
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"ada",
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],
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)
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def test_get_model_info_completion_cost_unit_tests(model):
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info = litellm.get_model_info(model)
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print("info", info)
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def test_get_model_info_ft_model_with_provider_prefix():
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args = {
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"model": "openai/ft:gpt-3.5-turbo:my-org:custom_suffix:id",
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"custom_llm_provider": "openai",
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}
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info = litellm.get_model_info(**args)
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print("info", info)
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assert info["key"] == "ft:gpt-3.5-turbo"
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def _enforce_bedrock_converse_models(
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model_cost: List[Dict[str, Any]], whitelist_models: List[str]
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):
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"""
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Assert all new bedrock chat models are added as `bedrock_converse` unless explicitly whitelisted.
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"""
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# Check for unwhitelisted models
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for model, info in litellm.model_cost.items():
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if (
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info["litellm_provider"] == "bedrock"
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and info["mode"] == "chat"
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and model not in whitelist_models
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):
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raise AssertionError(
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f"New bedrock chat model detected: {model}. Please set `litellm_provider='bedrock_converse'` for this model."
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)
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def test_model_info_bedrock_converse(monkeypatch):
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"""
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Assert all new bedrock chat models are added as `bedrock_converse` unless explicitly whitelisted.
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This ensures they are automatically routed to the converse endpoint.
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"""
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monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
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litellm.model_cost = litellm.get_model_cost_map(url="")
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try:
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# Load whitelist models from file
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with open("whitelisted_bedrock_models.txt", "r") as file:
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whitelist_models = [line.strip() for line in file.readlines()]
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except FileNotFoundError:
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pytest.skip("whitelisted_bedrock_models.txt not found")
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_enforce_bedrock_converse_models(
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model_cost=litellm.model_cost, whitelist_models=whitelist_models
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)
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@pytest.mark.flaky(retries=6, delay=2)
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def test_model_info_bedrock_converse_enforcement(monkeypatch):
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"""
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Test the enforcement of the whitelist by adding a fake model and ensuring the test fails.
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"""
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monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
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litellm.model_cost = litellm.get_model_cost_map(url="")
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# Add a fake unwhitelisted model
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litellm.model_cost["fake.bedrock-chat-model"] = {
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"litellm_provider": "bedrock",
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"mode": "chat",
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}
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try:
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# Load whitelist models from file
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with open("whitelisted_bedrock_models.txt", "r") as file:
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whitelist_models = [line.strip() for line in file.readlines()]
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# Check for unwhitelisted models
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with pytest.raises(AssertionError):
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_enforce_bedrock_converse_models(
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model_cost=litellm.model_cost, whitelist_models=whitelist_models
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)
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except FileNotFoundError as e:
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pytest.skip("whitelisted_bedrock_models.txt not found")
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def test_get_model_info_custom_provider():
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# Custom provider example copied from https://docs.litellm.ai/docs/providers/custom_llm_server:
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import litellm
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from litellm import CustomLLM, completion, get_llm_provider
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class MyCustomLLM(CustomLLM):
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def completion(self, *args, **kwargs) -> litellm.ModelResponse:
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return litellm.completion(
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model="gpt-3.5-turbo",
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messages=[{"role": "user", "content": "Hello world"}],
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mock_response="Hi!",
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) # type: ignore
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my_custom_llm = MyCustomLLM()
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litellm.custom_provider_map = [ # 👈 KEY STEP - REGISTER HANDLER
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{"provider": "my-custom-llm", "custom_handler": my_custom_llm}
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]
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resp = completion(
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model="my-custom-llm/my-fake-model",
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messages=[{"role": "user", "content": "Hello world!"}],
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)
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assert resp.choices[0].message.content == "Hi!"
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# Register model info
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model_info = {"my-custom-llm/my-fake-model": {"max_tokens": 2048}}
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litellm.register_model(model_info)
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# Get registered model info
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from litellm import get_model_info
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get_model_info(
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model="my-custom-llm/my-fake-model"
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) # 💥 "Exception: This model isn't mapped yet." in v1.56.10
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def test_get_model_info_custom_model_router():
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from litellm import Router
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from litellm import get_model_info
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litellm._turn_on_debug()
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router = Router(
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model_list=[
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{
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"model_name": "ma-summary",
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"litellm_params": {
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"api_base": "http://ma-mix-llm-serving.cicero.svc.cluster.local/v1",
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"input_cost_per_token": 1,
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"output_cost_per_token": 1,
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"model": "openai/meta-llama/Meta-Llama-3-8B-Instruct",
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},
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"model_info": {
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"id": "c20d603e-1166-4e0f-aa65-ed9c476ad4ca",
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}
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}
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]
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)
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info = get_model_info("c20d603e-1166-4e0f-aa65-ed9c476ad4ca")
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print("info", info)
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assert info is not None
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def test_get_model_info_bedrock_models():
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"""
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Check for drift in base model info for bedrock models and regional model info for bedrock models.
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"""
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from litellm.llms.bedrock.common_utils import BedrockModelInfo
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os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
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litellm.model_cost = litellm.get_model_cost_map(url="")
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for k, v in litellm.model_cost.items():
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if v["litellm_provider"] == "bedrock":
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k = k.replace("*/", "")
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potential_commitments = [
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"1-month-commitment",
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"3-month-commitment",
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"6-month-commitment",
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]
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if any(commitment in k for commitment in potential_commitments):
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for commitment in potential_commitments:
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k = k.replace(f"{commitment}/", "")
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base_model = BedrockModelInfo.get_base_model(k)
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base_model_info = litellm.model_cost[base_model]
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for base_model_key, base_model_value in base_model_info.items():
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if "invoke/" in k:
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continue
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if base_model_key.startswith("supports_"):
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assert (
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base_model_key in v
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), f"{base_model_key} is not in model cost map for {k}"
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assert (
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v[base_model_key] == base_model_value
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), f"{base_model_key} is not equal to {base_model_value} for model {k}"
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def test_get_model_info_huggingface_models(monkeypatch):
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from litellm import Router
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from litellm.types.router import ModelGroupInfo
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monkeypatch.setenv("HUGGINGFACE_API_KEY", "hf_abc123")
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router = Router(
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model_list=[
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{
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"model_name": "meta-llama/Meta-Llama-3-8B-Instruct",
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"litellm_params": {
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"model": "huggingface/meta-llama/Meta-Llama-3-8B-Instruct",
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"api_base": "https://router.huggingface.co/hf-inference/models/meta-llama/Meta-Llama-3-8B-Instruct",
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"api_key": os.environ["HUGGINGFACE_API_KEY"],
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},
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}
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]
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)
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info = litellm.get_model_info("huggingface/meta-llama/Meta-Llama-3-8B-Instruct")
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print("info", info)
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assert info is not None
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ModelGroupInfo(
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model_group="meta-llama/Meta-Llama-3-8B-Instruct",
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providers=["huggingface"],
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**info,
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)
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@pytest.mark.parametrize(
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"model, provider",
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[
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("bedrock/us-east-2/us.anthropic.claude-3-haiku-20240307-v1:0", None),
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(
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"bedrock/us-east-2/us.anthropic.claude-3-haiku-20240307-v1:0",
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"bedrock",
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),
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],
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)
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def test_get_model_info_cost_calculator_bedrock_region_cris_stripped(model, provider):
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"""
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ensure cross region inferencing model is used correctly
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Relevant Issue: https://github.com/BerriAI/litellm/issues/8115
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"""
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info = get_model_info(model=model, custom_llm_provider=provider)
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print("info", info)
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assert info["key"] == "us.anthropic.claude-3-haiku-20240307-v1:0"
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assert info["litellm_provider"] == "bedrock"
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