mirror of
https://github.com/BerriAI/litellm.git
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e9d40a8f73
A name bound twice keeps only the second binding. In `tests/` that is nearly always a repeated import, harmless but misleading, and the same rule is what catches the cases that are not harmless: a local that shadows an import the module still calls, and a second `def test_x` that quietly replaces the first. 311 of the 344 sites were repeated imports and came out with ruff's own fix. The remaining 33 needed a decision. Four modules imported a name they never used because a local definition below already shadowed it. Two comprehensions bound `call` over `unittest.mock.call`, which those modules import and use. One test rebound the two module handles its nested reload closure had captured. One class attribute shadowed an unused `status` import. The load-test fixtures move to a conftest, which is how pytest is meant to share them, so the test module no longer imports three fixture names it never calls. The nine `prisma_client` parameters keep a narrow `noqa`: pytest resolves that fixture by name before the body runs, so the parameter never shadows anything.
484 lines
16 KiB
Python
484 lines
16 KiB
Python
import json
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import os
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import sys
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import traceback
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from dotenv import load_dotenv
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load_dotenv()
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import io
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import litellm
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from test_streaming import streaming_format_tests
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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 os
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from unittest.mock import AsyncMock, MagicMock, patch
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import pytest
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from litellm import RateLimitError, Timeout, completion, completion_cost, embedding
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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from litellm.litellm_core_utils.prompt_templates.factory import anthropic_messages_pt
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# litellm.num_retries =3
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litellm.cache = None
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litellm.success_callback = []
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user_message = "Write a short poem about the sky"
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messages = [{"content": user_message, "role": "user"}]
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import logging
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from litellm._logging import verbose_logger
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def logger_fn(user_model_dict):
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print(f"user_model_dict: {user_model_dict}")
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@pytest.fixture(autouse=True)
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def reset_callbacks():
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print("\npytest fixture - resetting callbacks")
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litellm.success_callback = []
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litellm._async_success_callback = []
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litellm.failure_callback = []
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litellm.callbacks = []
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@pytest.mark.asyncio()
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@pytest.mark.parametrize("sync_mode", [True, False])
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async def test_completion_sagemaker(sync_mode):
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try:
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litellm.set_verbose = True
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verbose_logger.setLevel(logging.DEBUG)
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print("testing sagemaker")
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if sync_mode is True:
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response = litellm.completion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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else:
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response = await litellm.acompletion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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# Add any assertions here to check the response
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print(response)
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cost = completion_cost(completion_response=response)
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print("calculated cost", cost)
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assert (
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cost > 0.0 and cost < 1.0
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) # should never be > $1 for a single completion call
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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@pytest.mark.asyncio()
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@pytest.mark.parametrize(
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"sync_mode",
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[True, False],
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)
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async def test_completion_sagemaker_messages_api(sync_mode):
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try:
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litellm.set_verbose = True
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verbose_logger.setLevel(logging.DEBUG)
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print("testing sagemaker")
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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if sync_mode is True:
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client = HTTPHandler()
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with patch.object(client, "post") as mock_post:
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try:
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resp = litellm.completion(
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model="sagemaker_chat/huggingface-pytorch-tgi-inference-2024-08-23-15-48-59-245",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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client=client,
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)
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except Exception as e:
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print(e)
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mock_post.assert_called_once()
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json_data = json.loads(mock_post.call_args.kwargs["data"])
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assert (
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json_data["model"]
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== "huggingface-pytorch-tgi-inference-2024-08-23-15-48-59-245"
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)
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assert json_data["messages"] == [{"role": "user", "content": "hi"}]
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assert json_data["temperature"] == 0.2
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assert json_data["max_tokens"] == 80
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else:
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client = AsyncHTTPHandler()
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with patch.object(client, "post") as mock_post:
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try:
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resp = await litellm.acompletion(
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model="sagemaker_chat/huggingface-pytorch-tgi-inference-2024-08-23-15-48-59-245",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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num_retries=0,
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client=client,
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)
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except Exception as e:
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print(e)
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mock_post.assert_called_once()
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json_data = json.loads(mock_post.call_args.kwargs["data"])
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assert (
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json_data["model"]
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== "huggingface-pytorch-tgi-inference-2024-08-23-15-48-59-245"
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)
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assert json_data["messages"] == [{"role": "user", "content": "hi"}]
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assert json_data["temperature"] == 0.2
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assert json_data["max_tokens"] == 80
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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@pytest.mark.asyncio()
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@pytest.mark.parametrize("sync_mode", [False, True])
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@pytest.mark.parametrize(
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"model",
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[
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# "sagemaker_chat/huggingface-pytorch-tgi-inference-2024-08-23-15-48-59-245",
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"sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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],
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)
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# @pytest.mark.flaky(retries=3, delay=1)
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async def test_completion_sagemaker_stream(sync_mode, model):
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try:
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litellm.set_verbose = False
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print("testing sagemaker")
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verbose_logger.setLevel(logging.DEBUG)
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full_text = ""
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if sync_mode is True:
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response = litellm.completion(
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model=model,
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messages=[
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{"role": "user", "content": "hi - what is ur name"},
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],
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temperature=0.2,
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stream=True,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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for idx, chunk in enumerate(response):
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print(chunk)
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streaming_format_tests(idx=idx, chunk=chunk)
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full_text += chunk.choices[0].delta.content or ""
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print("SYNC RESPONSE full text", full_text)
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else:
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response = await litellm.acompletion(
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model=model,
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messages=[
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{"role": "user", "content": "hi - what is ur name"},
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],
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stream=True,
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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print("streaming response")
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idx = 0
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async for chunk in response:
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print(chunk)
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streaming_format_tests(idx=idx, chunk=chunk)
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full_text += chunk.choices[0].delta.content or ""
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idx += 1
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print("ASYNC RESPONSE full text", full_text)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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@pytest.mark.asyncio()
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@pytest.mark.parametrize("sync_mode", [False, True])
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@pytest.mark.parametrize(
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"model",
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[
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# "sagemaker_chat/huggingface-pytorch-tgi-inference-2024-08-23-15-48-59-245",
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"sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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],
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)
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async def test_completion_sagemaker_streaming_bad_request(sync_mode, model):
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litellm.set_verbose = True
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print("testing sagemaker")
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if sync_mode is True:
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with pytest.raises(litellm.BadRequestError):
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response = litellm.completion(
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model=model,
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messages=[
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{"role": "user", "content": "hi"},
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],
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stream=True,
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max_tokens=8000000000000000,
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)
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else:
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with pytest.raises(litellm.BadRequestError):
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response = await litellm.acompletion(
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model=model,
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messages=[
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{"role": "user", "content": "hi"},
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],
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stream=True,
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max_tokens=8000000000000000,
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)
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@pytest.mark.asyncio
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async def test_acompletion_sagemaker_non_stream():
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mock_response = AsyncMock()
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def return_val():
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return {
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"generated_text": "This is a mock response from SageMaker.",
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"id": "cmpl-mockid",
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"object": "text_completion",
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"created": 1629800000,
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"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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"choices": [
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{
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"text": "This is a mock response from SageMaker.",
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"index": 0,
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"logprobs": None,
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"finish_reason": "length",
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}
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],
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"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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expected_payload = {
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"inputs": "hi",
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"parameters": {"temperature": 0.2, "max_new_tokens": 80},
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.AsyncHTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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# Act: Call the litellm.acompletion function
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response = await litellm.acompletion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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# Print what was called on the mock
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print("call args=", mock_post.call_args)
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# Assert
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mock_post.assert_called_once()
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_, kwargs = mock_post.call_args
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args_to_sagemaker = json.loads(kwargs["data"])
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print("Arguments passed to sagemaker=", args_to_sagemaker)
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assert args_to_sagemaker == expected_payload
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assert (
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kwargs["url"]
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== "https://runtime.sagemaker.us-west-2.amazonaws.com/endpoints/jumpstart-dft-hf-textgeneration1-mp-20240815-185614/invocations"
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)
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@pytest.mark.asyncio
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async def test_completion_sagemaker_non_stream():
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mock_response = MagicMock()
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def return_val():
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return {
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"generated_text": "This is a mock response from SageMaker.",
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"id": "cmpl-mockid",
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"object": "text_completion",
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"created": 1629800000,
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"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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"choices": [
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{
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"text": "This is a mock response from SageMaker.",
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"index": 0,
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"logprobs": None,
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"finish_reason": "length",
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}
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],
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"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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expected_payload = {
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"inputs": "hi",
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"parameters": {"temperature": 0.2, "max_new_tokens": 80},
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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# Act: Call the litellm.acompletion function
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response = litellm.completion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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)
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# Print what was called on the mock
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print("call args=", mock_post.call_args)
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# Assert
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mock_post.assert_called_once()
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_, kwargs = mock_post.call_args
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args_to_sagemaker = json.loads(kwargs["data"])
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print("Arguments passed to sagemaker=", args_to_sagemaker)
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assert args_to_sagemaker == expected_payload
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assert (
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kwargs["url"]
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== "https://runtime.sagemaker.us-west-2.amazonaws.com/endpoints/jumpstart-dft-hf-textgeneration1-mp-20240815-185614/invocations"
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)
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@pytest.mark.asyncio
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@pytest.mark.flaky(retries=3, delay=1)
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async def test_completion_sagemaker_prompt_template_non_stream():
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mock_response = MagicMock()
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def return_val():
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return {
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"generated_text": "This is a mock response from SageMaker.",
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"id": "cmpl-mockid",
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"object": "text_completion",
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"created": 1629800000,
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"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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"choices": [
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{
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"text": "This is a mock response from SageMaker.",
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"index": 0,
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"logprobs": None,
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"finish_reason": "length",
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}
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],
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"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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expected_payload = {
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"inputs": "<｜begin▁of▁sentence｜>You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer\n\n### Instruction:\nhi\n\n\n### Response:\n",
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"parameters": {"temperature": 0.2, "max_new_tokens": 80},
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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# Act: Call the litellm.acompletion function
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response = litellm.completion(
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model="sagemaker/deepseek_coder_6.7_instruct",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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hf_model_name="deepseek-ai/deepseek-coder-6.7b-instruct",
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)
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# Print what was called on the mock
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print("call args=", mock_post.call_args)
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# Assert
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mock_post.assert_called_once()
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_, kwargs = mock_post.call_args
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args_to_sagemaker = json.loads(kwargs["data"])
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print("Arguments passed to sagemaker=", args_to_sagemaker)
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assert args_to_sagemaker == expected_payload
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@pytest.mark.asyncio
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async def test_completion_sagemaker_non_stream_with_aws_params():
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mock_response = MagicMock()
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def return_val():
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return {
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"generated_text": "This is a mock response from SageMaker.",
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"id": "cmpl-mockid",
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"object": "text_completion",
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"created": 1629800000,
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"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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"choices": [
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{
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"text": "This is a mock response from SageMaker.",
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"index": 0,
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"logprobs": None,
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"finish_reason": "length",
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}
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],
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"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
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}
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mock_response.json = return_val
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mock_response.status_code = 200
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expected_payload = {
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"inputs": "hi",
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"parameters": {"temperature": 0.2, "max_new_tokens": 80},
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}
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with patch(
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"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
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return_value=mock_response,
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) as mock_post:
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# Act: Call the litellm.acompletion function
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response = litellm.completion(
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model="sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
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messages=[
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{"role": "user", "content": "hi"},
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],
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temperature=0.2,
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max_tokens=80,
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input_cost_per_second=0.000420,
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aws_access_key_id="gm",
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aws_secret_access_key="s",
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aws_region_name="us-west-5",
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)
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# Print what was called on the mock
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print("call args=", mock_post.call_args)
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# Assert
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mock_post.assert_called_once()
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_, kwargs = mock_post.call_args
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args_to_sagemaker = json.loads(kwargs["data"])
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print("Arguments passed to sagemaker=", args_to_sagemaker)
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assert args_to_sagemaker == expected_payload
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assert (
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kwargs["url"]
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== "https://runtime.sagemaker.us-west-5.amazonaws.com/endpoints/jumpstart-dft-hf-textgeneration1-mp-20240815-185614/invocations"
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)
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