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litellm-mirror/litellm/tests/test_anthropic_completion.py
Krrish Dholakia 059a161c28 style: initial commit
2024-07-09 13:38:33 -07:00

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# What is this?
## Unit tests for Anthropic Adapter
# import asyncio
# import os
# import sys
# import traceback
# from dotenv import load_dotenv
# load_dotenv()
# import io
# import os
# sys.path.insert(
# 0, os.path.abspath("../..")
# ) # Adds the parent directory to the system path
# from unittest.mock import MagicMock, patch
# import pytest
# import litellm
# from litellm import (
# RateLimitError,
# TextCompletionResponse,
# atext_completion,
# completion,
# completion_cost,
# embedding,
# text_completion,
# )
# litellm.num_retries = 3
# token_prompt = [
# [
# 32,
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# ]
# def test_unit_test_text_completion_object():
# openai_object = {
# "id": "cmpl-99y7B2svVoRWe1xd7UFRmeGjZrFSh",
# "choices": [
# {
# "finish_reason": "length",
# "index": 0,
# "logprobs": {
# "text_offset": [101],
# "token_logprobs": [-0.00023488728],
# "tokens": ["0"],
# "top_logprobs": [
# {
# "0": -0.00023488728,
# "1": -8.375235,
# "zero": -14.101797,
# "__": -14.554922,
# "00": -14.98461,
# }
# ],
# },
# "text": "0",
# },
# {
# "finish_reason": "length",
# "index": 1,
# "logprobs": {
# "text_offset": [116],
# "token_logprobs": [-0.013745008],
# "tokens": ["0"],
# "top_logprobs": [
# {
# "0": -0.013745008,
# "1": -4.294995,
# "00": -12.287183,
# "2": -12.771558,
# "3": -14.013745,
# }
# ],
# },
# "text": "0",
# },
# {
# "finish_reason": "length",
# "index": 2,
# "logprobs": {
# "text_offset": [108],
# "token_logprobs": [-3.655073e-5],
# "tokens": ["0"],
# "top_logprobs": [
# {
# "0": -3.655073e-5,
# "1": -10.656286,
# "__": -11.789099,
# "false": -12.984411,
# "00": -14.039099,
# }
# ],
# },
# "text": "0",
# },
# {
# "finish_reason": "length",
# "index": 3,
# "logprobs": {
# "text_offset": [106],
# "token_logprobs": [-0.1345946],
# "tokens": ["0"],
# "top_logprobs": [
# {
# "0": -0.1345946,
# "1": -2.0720947,
# "2": -12.798657,
# "false": -13.970532,
# "00": -14.27522,
# }
# ],
# },
# "text": "0",
# },
# {
# "finish_reason": "length",
# "index": 4,
# "logprobs": {
# "text_offset": [95],
# "token_logprobs": [-0.10491652],
# "tokens": ["0"],
# "top_logprobs": [
# {
# "0": -0.10491652,
# "1": -2.3236666,
# "2": -7.0111666,
# "3": -7.987729,
# "4": -9.050229,
# }
# ],
# },
# "text": "0",
# },
# {
# "finish_reason": "length",
# "index": 5,
# "logprobs": {
# "text_offset": [121],
# "token_logprobs": [-0.00026300468],
# "tokens": ["0"],
# "top_logprobs": [
# {
# "0": -0.00026300468,
# "1": -8.250263,
# "zero": -14.976826,
# " ": -15.461201,
# "000": -15.773701,
# }
# ],
# },
# "text": "0",
# },
# {
# "finish_reason": "length",
# "index": 6,
# "logprobs": {
# "text_offset": [146],
# "token_logprobs": [-5.085517e-5],
# "tokens": ["0"],
# "top_logprobs": [
# {
# "0": -5.085517e-5,
# "1": -9.937551,
# "000": -13.929738,
# "__": -14.968801,
# "zero": -15.070363,
# }
# ],
# },
# "text": "0",
# },
# {
# "finish_reason": "length",
# "index": 7,
# "logprobs": {
# "text_offset": [100],
# "token_logprobs": [-0.13875218],
# "tokens": ["1"],
# "top_logprobs": [
# {
# "1": -0.13875218,
# "0": -2.0450022,
# "2": -9.7559395,
# "3": -11.1465645,
# "4": -11.5528145,
# }
# ],
# },
# "text": "1",
# },
# {
# "finish_reason": "length",
# "index": 8,
# "logprobs": {
# "text_offset": [143],
# "token_logprobs": [-0.0005573204],
# "tokens": ["0"],
# "top_logprobs": [
# {
# "0": -0.0005573204,
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# "created": 1712163061,
# "model": "ft:babbage-002:ai-r-d-zapai:v3-fields-used:84jb9rtr",
# "object": "text_completion",
# "system_fingerprint": None,
# "usage": {"completion_tokens": 54, "prompt_tokens": 1877, "total_tokens": 1931},
# }
# text_completion_obj = TextCompletionResponse(**openai_object)
# ## WRITE UNIT TESTS FOR TEXT_COMPLETION_OBJECT
# assert text_completion_obj.id == "cmpl-99y7B2svVoRWe1xd7UFRmeGjZrFSh"
# assert text_completion_obj.object == "text_completion"
# assert text_completion_obj.created == 1712163061
# assert (
# text_completion_obj.model
# == "ft:babbage-002:ai-r-d-zapai:v3-fields-used:84jb9rtr"
# )
# assert text_completion_obj.system_fingerprint == None
# assert len(text_completion_obj.choices) == len(openai_object["choices"])
# # TEST FIRST CHOICE #
# first_text_completion_obj = text_completion_obj.choices[0]
# assert first_text_completion_obj.index == 0
# assert first_text_completion_obj.logprobs.text_offset == [101]
# assert first_text_completion_obj.logprobs.tokens == ["0"]
# assert first_text_completion_obj.logprobs.token_logprobs == [-0.00023488728]
# assert len(first_text_completion_obj.logprobs.top_logprobs) == len(
# openai_object["choices"][0]["logprobs"]["top_logprobs"]
# )
# assert first_text_completion_obj.text == "0"
# assert first_text_completion_obj.finish_reason == "length"
# # TEST SECOND CHOICE #
# second_text_completion_obj = text_completion_obj.choices[1]
# assert second_text_completion_obj.index == 1
# assert second_text_completion_obj.logprobs.text_offset == [116]
# assert second_text_completion_obj.logprobs.tokens == ["0"]
# assert second_text_completion_obj.logprobs.token_logprobs == [-0.013745008]
# assert len(second_text_completion_obj.logprobs.top_logprobs) == len(
# openai_object["choices"][0]["logprobs"]["top_logprobs"]
# )
# assert second_text_completion_obj.text == "0"
# assert second_text_completion_obj.finish_reason == "length"
# # TEST LAST CHOICE #
# last_text_completion_obj = text_completion_obj.choices[-1]
# assert last_text_completion_obj.index == 53
# assert last_text_completion_obj.logprobs.text_offset == [143]
# assert last_text_completion_obj.logprobs.tokens == ["0"]
# assert last_text_completion_obj.logprobs.token_logprobs == [-0.0012339224]
# assert len(last_text_completion_obj.logprobs.top_logprobs) == len(
# openai_object["choices"][0]["logprobs"]["top_logprobs"]
# )
# assert last_text_completion_obj.text == "0"
# assert last_text_completion_obj.finish_reason == "length"
# assert text_completion_obj.usage.completion_tokens == 54
# assert text_completion_obj.usage.prompt_tokens == 1877
# assert text_completion_obj.usage.total_tokens == 1931
# def test_completion_openai_prompt():
# try:
# print("\n text 003 test\n")
# response = text_completion(
# model="gpt-3.5-turbo-instruct",
# prompt=["What's the weather in SF?", "How is Manchester?"],
# )
# print(response)
# assert len(response.choices) == 2
# response_str = response["choices"][0]["text"]
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# # test_completion_openai_prompt()
# def test_completion_openai_engine_and_model():
# try:
# print("\n text 003 test\n")
# litellm.set_verbose = True
# response = text_completion(
# model="gpt-3.5-turbo-instruct",
# engine="anything",
# prompt="What's the weather in SF?",
# max_tokens=5,
# )
# print(response)
# response_str = response["choices"][0]["text"]
# # print(response.choices[0])
# # print(response.choices[0].text)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# # test_completion_openai_engine_and_model()
# def test_completion_openai_engine():
# try:
# print("\n text 003 test\n")
# litellm.set_verbose = True
# response = text_completion(
# engine="gpt-3.5-turbo-instruct",
# prompt="What's the weather in SF?",
# max_tokens=5,
# )
# print(response)
# response_str = response["choices"][0]["text"]
# # print(response.choices[0])
# # print(response.choices[0].text)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# # test_completion_openai_engine()
# def test_completion_chatgpt_prompt():
# try:
# print("\n gpt3.5 test\n")
# response = text_completion(
# model="gpt-3.5-turbo", prompt="What's the weather in SF?"
# )
# print(response)
# response_str = response["choices"][0]["text"]
# print("\n", response.choices)
# print("\n", response.choices[0])
# # print(response.choices[0].text)
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# # test_completion_chatgpt_prompt()
# def test_text_completion_basic():
# try:
# print("\n test 003 with logprobs \n")
# litellm.set_verbose = False
# response = text_completion(
# model="gpt-3.5-turbo-instruct",
# prompt="good morning",
# max_tokens=10,
# logprobs=10,
# )
# print(response)
# print(response.choices)
# print(response.choices[0])
# # print(response.choices[0].text)
# response_str = response["choices"][0]["text"]
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# # test_text_completion_basic()
# def test_completion_text_003_prompt_array():
# try:
# litellm.set_verbose = False
# response = text_completion(
# model="gpt-3.5-turbo-instruct",
# prompt=token_prompt, # token prompt is a 2d list
# )
# print("\n\n response")
# print(response)
# # response_str = response["choices"][0]["text"]
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# # test_completion_text_003_prompt_array()
# # not including this in our ci cd pipeline, since we don't want to fail tests due to an unstable replit
# # def test_text_completion_with_proxy():
# # try:
# # litellm.set_verbose=True
# # response = text_completion(
# # model="facebook/opt-125m",
# # prompt='Write a tagline for a traditional bavarian tavern',
# # api_base="https://openai-proxy.berriai.repl.co/v1",
# # custom_llm_provider="openai",
# # temperature=0,
# # max_tokens=10,
# # )
# # print("\n\n response")
# # print(response)
# # except Exception as e:
# # pytest.fail(f"Error occurred: {e}")
# # test_text_completion_with_proxy()
# ##### hugging face tests
# def test_completion_hf_prompt_array():
# try:
# litellm.set_verbose = True
# print("\n testing hf mistral\n")
# response = text_completion(
# model="huggingface/mistralai/Mistral-7B-v0.1",
# prompt=token_prompt, # token prompt is a 2d list,
# max_tokens=0,
# temperature=0.0,
# # echo=True, # hugging face inference api is currently raising errors for this, looks like they have a regression on their side
# )
# print("\n\n response")
# print(response)
# print(response.choices)
# assert len(response.choices) == 2
# # response_str = response["choices"][0]["text"]
# except Exception as e:
# print(str(e))
# if "is currently loading" in str(e):
# return
# if "Service Unavailable" in str(e):
# return
# pytest.fail(f"Error occurred: {e}")
# # test_completion_hf_prompt_array()
# def test_text_completion_stream():
# try:
# response = text_completion(
# model="huggingface/mistralai/Mistral-7B-v0.1",
# prompt="good morning",
# stream=True,
# max_tokens=10,
# )
# for chunk in response:
# print(f"chunk: {chunk}")
# except Exception as e:
# pytest.fail(f"GOT exception for HF In streaming{e}")
# # test_text_completion_stream()
# # async def test_text_completion_async_stream():
# # try:
# # response = await atext_completion(
# # model="text-completion-openai/gpt-3.5-turbo-instruct",
# # prompt="good morning",
# # stream=True,
# # max_tokens=10,
# # )
# # async for chunk in response:
# # print(f"chunk: {chunk}")
# # except Exception as e:
# # pytest.fail(f"GOT exception for HF In streaming{e}")
# # asyncio.run(test_text_completion_async_stream())
# def test_async_text_completion():
# litellm.set_verbose = True
# print("test_async_text_completion")
# async def test_get_response():
# try:
# response = await litellm.atext_completion(
# model="gpt-3.5-turbo-instruct",
# prompt="good morning",
# stream=False,
# max_tokens=10,
# )
# print(f"response: {response}")
# except litellm.Timeout as e:
# print(e)
# except Exception as e:
# print(e)
# asyncio.run(test_get_response())
# @pytest.mark.skip(reason="Skip flaky tgai test")
# def test_async_text_completion_together_ai():
# litellm.set_verbose = True
# print("test_async_text_completion")
# async def test_get_response():
# try:
# response = await litellm.atext_completion(
# model="together_ai/mistralai/Mixtral-8x7B-Instruct-v0.1",
# prompt="good morning",
# max_tokens=10,
# )
# print(f"response: {response}")
# except litellm.Timeout as e:
# print(e)
# except Exception as e:
# pytest.fail("An unexpected error occurred")
# asyncio.run(test_get_response())
# # test_async_text_completion()
# def test_async_text_completion_stream():
# # tests atext_completion + streaming - assert only one finish reason sent
# litellm.set_verbose = False
# print("test_async_text_completion with stream")
# async def test_get_response():
# try:
# response = await litellm.atext_completion(
# model="gpt-3.5-turbo-instruct",
# prompt="good morning",
# stream=True,
# )
# print(f"response: {response}")
# num_finish_reason = 0
# async for chunk in response:
# print(chunk)
# if chunk["choices"][0].get("finish_reason") is not None:
# num_finish_reason += 1
# print("finish_reason", chunk["choices"][0].get("finish_reason"))
# assert (
# num_finish_reason == 1
# ), f"expected only one finish reason. Got {num_finish_reason}"
# except Exception as e:
# pytest.fail(f"GOT exception for gpt-3.5 instruct In streaming{e}")
# asyncio.run(test_get_response())
# # test_async_text_completion_stream()
# @pytest.mark.asyncio
# async def test_async_text_completion_chat_model_stream():
# try:
# response = await litellm.atext_completion(
# model="gpt-3.5-turbo",
# prompt="good morning",
# stream=True,
# max_tokens=10,
# )
# num_finish_reason = 0
# chunks = []
# async for chunk in response:
# print(chunk)
# chunks.append(chunk)
# if chunk["choices"][0].get("finish_reason") is not None:
# num_finish_reason += 1
# assert (
# num_finish_reason == 1
# ), f"expected only one finish reason. Got {num_finish_reason}"
# response_obj = litellm.stream_chunk_builder(chunks=chunks)
# cost = litellm.completion_cost(completion_response=response_obj)
# assert cost > 0
# except Exception as e:
# pytest.fail(f"GOT exception for gpt-3.5 In streaming{e}")
# # asyncio.run(test_async_text_completion_chat_model_stream())
# @pytest.mark.asyncio
# async def test_completion_codestral_fim_api():
# try:
# litellm.set_verbose = True
# import logging
# from litellm._logging import verbose_logger
# verbose_logger.setLevel(level=logging.DEBUG)
# response = await litellm.atext_completion(
# model="text-completion-codestral/codestral-2405",
# prompt="def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():",
# suffix="return True",
# temperature=0,
# top_p=1,
# max_tokens=10,
# min_tokens=10,
# seed=10,
# stop=["return"],
# )
# # Add any assertions here to check the response
# print(response)
# assert response.choices[0].text is not None
# assert len(response.choices[0].text) > 0
# # cost = litellm.completion_cost(completion_response=response)
# # print("cost to make mistral completion=", cost)
# # assert cost > 0.0
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# @pytest.mark.asyncio
# async def test_completion_codestral_fim_api_stream():
# try:
# import logging
# from litellm._logging import verbose_logger
# litellm.set_verbose = False
# # verbose_logger.setLevel(level=logging.DEBUG)
# response = await litellm.atext_completion(
# model="text-completion-codestral/codestral-2405",
# prompt="def is_odd(n): \n return n % 2 == 1 \ndef test_is_odd():",
# suffix="return True",
# temperature=0,
# top_p=1,
# stream=True,
# seed=10,
# stop=["return"],
# )
# full_response = ""
# # Add any assertions here to check the response
# async for chunk in response:
# print(chunk)
# full_response += chunk.get("choices")[0].get("text") or ""
# print("full_response", full_response)
# assert len(full_response) > 2 # we at least have a few chars in response :)
# # cost = litellm.completion_cost(completion_response=response)
# # print("cost to make mistral completion=", cost)
# # assert cost > 0.0
# except Exception as e:
# pytest.fail(f"Error occurred: {e}")
# def mock_post(*args, **kwargs):
# mock_response = MagicMock()
# mock_response.status_code = 200
# mock_response.headers = {"Content-Type": "application/json"}
# mock_response.model_dump.return_value = {
# "id": "cmpl-7a59383dd4234092b9e5d652a7ab8143",
# "object": "text_completion",
# "created": 1718824735,
# "model": "Sao10K/L3-70B-Euryale-v2.1",
# "choices": [
# {
# "index": 0,
# "text": ") might be faster than then answering, and the added time it takes for the",
# "logprobs": None,
# "finish_reason": "length",
# "stop_reason": None,
# }
# ],
# "usage": {"prompt_tokens": 2, "total_tokens": 18, "completion_tokens": 16},
# }
# return mock_response
# def test_completion_vllm():
# """
# Asserts a text completion call for vllm actually goes to the text completion endpoint
# """
# from openai import OpenAI
# client = OpenAI(api_key="my-fake-key")
# with patch.object(client.completions, "create", side_effect=mock_post) as mock_call:
# response = text_completion(
# model="openai/gemini-1.5-flash", prompt="ping", client=client, hello="world"
# )
# print(response)
# assert response.usage.prompt_tokens == 2
# mock_call.assert_called_once()
# assert "hello" in mock_call.call_args.kwargs["extra_body"]
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