mirror of
https://github.com/BerriAI/litellm.git
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6a0d03914c
* test: drop the cwd-relative sys.path.insert calls from the test suite
TQ003 stands at 1,077 across 1,058 files, and 1,015 of them are the same shape:
sys.path.insert(0, os.path.abspath("../..")) and its deeper siblings. The
argument resolves against the working directory rather than the file, so from
the repo root, where every job runs pytest, it inserts the directory two levels
above the checkout. It has never pointed at litellm. The package is installed
into the environment anyway, which is what actually makes the import work, and
what the rule's message has said all along.
Removing them leaves 1,634 imports of sys and os with no remaining reference,
and those go too, except where another test module imports the name back out of
the file. The rest of TQ003 is 62 call sites that resolve against __file__ or a
variable, which are a different question and are left alone.
Collection is identical either way: 45,871 tests and the same 51 pre-existing
collection errors before and after, and ruff reports no new undefined name.
* test: drop the duplicate imports the sys.path sweep exposed to F811
* test(pre-call-utils): restore the os import the new bedrock tests need
478 lines
16 KiB
Python
478 lines
16 KiB
Python
import json
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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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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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