mirror of
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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
758 lines
24 KiB
Python
758 lines
24 KiB
Python
#### What this tests ####
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# This tests setting provider specific configs across providers
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# There are 2 types of tests - changing config dynamically or by setting class variables
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import os
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import traceback
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import json
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import pytest
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from unittest.mock import AsyncMock, MagicMock, patch
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import litellm
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from litellm import RateLimitError, completion
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# Huggingface - Expensive to deploy models and keep them running. Maybe we can try doing this via baseten??
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# def hf_test_completion_tgi():
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# litellm.HuggingfaceConfig(max_new_tokens=200)
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# litellm.set_verbose=True
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# try:
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# # OVERRIDE WITH DYNAMIC MAX TOKENS
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# response_1 = litellm.completion(
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# model="huggingface/mistralai/Mistral-7B-Instruct-v0.1",
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# messages=[{ "content": "Hello, how are you?","role": "user"}],
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# api_base="https://n9ox93a8sv5ihsow.us-east-1.aws.endpoints.huggingface.cloud",
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# max_tokens=10
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# )
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# # Add any assertions here to check the response
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# print(response_1)
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# response_1_text = response_1.choices[0].message.content
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# # USE CONFIG TOKENS
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# response_2 = litellm.completion(
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# model="huggingface/mistralai/Mistral-7B-Instruct-v0.1",
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# messages=[{ "content": "Hello, how are you?","role": "user"}],
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# api_base="https://n9ox93a8sv5ihsow.us-east-1.aws.endpoints.huggingface.cloud",
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# )
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# # Add any assertions here to check the response
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# print(response_2)
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# response_2_text = response_2.choices[0].message.content
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# assert len(response_2_text) > len(response_1_text)
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# hf_test_completion_tgi()
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# Anthropic
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def claude_test_completion():
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litellm.AnthropicConfig(max_tokens_to_sample=200)
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# litellm.set_verbose=True
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try:
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# OVERRIDE WITH DYNAMIC MAX TOKENS
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response_1 = litellm.completion(
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model="claude-3-haiku-20240307",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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max_tokens=10,
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)
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# Add any assertions here to check the response
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print(response_1)
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response_1_text = response_1.choices[0].message.content
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# USE CONFIG TOKENS
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response_2 = litellm.completion(
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model="claude-3-haiku-20240307",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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)
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# Add any assertions here to check the response
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print(response_2)
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response_2_text = response_2.choices[0].message.content
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assert len(response_2_text) > len(response_1_text)
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try:
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response_3 = litellm.completion(
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model="claude-3-5-haiku-20241022",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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n=2,
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)
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except Exception as e:
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print(e)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# claude_test_completion()
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# Replicate
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def replicate_test_completion():
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litellm.ReplicateConfig(max_new_tokens=200)
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# litellm.set_verbose=True
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try:
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# OVERRIDE WITH DYNAMIC MAX TOKENS
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response_1 = litellm.completion(
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model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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max_tokens=10,
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)
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# Add any assertions here to check the response
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print(response_1)
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response_1_text = response_1.choices[0].message.content
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# USE CONFIG TOKENS
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response_2 = litellm.completion(
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model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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)
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# Add any assertions here to check the response
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print(response_2)
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response_2_text = response_2.choices[0].message.content
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assert len(response_2_text) > len(response_1_text)
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try:
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response_3 = litellm.completion(
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model="meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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n=2,
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)
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except Exception:
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pass
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# replicate_test_completion()
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# Cohere
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def cohere_test_completion():
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# litellm.CohereConfig(max_tokens=200)
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litellm.set_verbose = True
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try:
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# OVERRIDE WITH DYNAMIC MAX TOKENS
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response_1 = litellm.completion(
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model="command-nightly",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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max_tokens=10,
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)
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response_1_text = response_1.choices[0].message.content
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# USE CONFIG TOKENS
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response_2 = litellm.completion(
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model="command-nightly",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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)
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response_2_text = response_2.choices[0].message.content
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assert len(response_2_text) > len(response_1_text)
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response_3 = litellm.completion(
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model="command-nightly",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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n=2,
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)
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assert len(response_3.choices) > 1
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# cohere_test_completion()
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# ai21_test_completion()
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# TogetherAI
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def togetherai_test_completion():
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litellm.TogetherAIConfig(max_tokens=10)
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litellm.set_verbose = True
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try:
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# OVERRIDE WITH DYNAMIC MAX TOKENS
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response_1 = litellm.completion(
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model="together_ai/togethercomputer/llama-2-70b-chat",
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messages=[
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{
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"content": "Hello, how are you? Be as verbose as possible",
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"role": "user",
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}
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],
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max_tokens=100,
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)
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response_1_text = response_1.choices[0].message.content
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print(f"response_1_text: {response_1_text}")
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# USE CONFIG TOKENS
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response_2 = litellm.completion(
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model="together_ai/togethercomputer/llama-2-70b-chat",
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messages=[
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{
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"content": "Hello, how are you? Be as verbose as possible",
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"role": "user",
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}
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],
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)
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response_2_text = response_2.choices[0].message.content
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print(f"response_2_text: {response_2_text}")
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assert len(response_2_text) < len(response_1_text)
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try:
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response_3 = litellm.completion(
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model="together_ai/togethercomputer/llama-2-70b-chat",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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n=2,
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)
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pytest.fail(f"Error not raised when n=2 passed to provider")
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except Exception:
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pass
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# togetherai_test_completion()
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# Palm
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# palm_test_completion()
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# NLP Cloud
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def nlp_cloud_test_completion():
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litellm.NLPCloudConfig(max_length=10)
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# litellm.set_verbose=True
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try:
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# OVERRIDE WITH DYNAMIC MAX TOKENS
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response_1 = litellm.completion(
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model="dolphin",
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messages=[
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{
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"content": "Hello, how are you? Be as verbose as possible",
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"role": "user",
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}
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],
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max_tokens=100,
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)
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response_1_text = response_1.choices[0].message.content
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print(f"response_1_text: {response_1_text}")
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# USE CONFIG TOKENS
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response_2 = litellm.completion(
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model="dolphin",
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messages=[
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{
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"content": "Hello, how are you? Be as verbose as possible",
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"role": "user",
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}
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],
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)
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response_2_text = response_2.choices[0].message.content
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print(f"response_2_text: {response_2_text}")
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assert len(response_2_text) < len(response_1_text)
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try:
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response_3 = litellm.completion(
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model="dolphin",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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n=2,
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)
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pytest.fail(f"Error not raised when n=2 passed to provider")
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except Exception:
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pass
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# nlp_cloud_test_completion()
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# AlephAlpha
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def aleph_alpha_test_completion():
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litellm.AlephAlphaConfig(maximum_tokens=10)
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# litellm.set_verbose=True
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try:
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# OVERRIDE WITH DYNAMIC MAX TOKENS
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response_1 = litellm.completion(
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model="luminous-base",
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messages=[
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{
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"content": "Hello, how are you? Be as verbose as possible",
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"role": "user",
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}
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],
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max_tokens=100,
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)
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response_1_text = response_1.choices[0].message.content
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print(f"response_1_text: {response_1_text}")
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# USE CONFIG TOKENS
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response_2 = litellm.completion(
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model="luminous-base",
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messages=[
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{
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"content": "Hello, how are you? Be as verbose as possible",
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"role": "user",
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}
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],
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)
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response_2_text = response_2.choices[0].message.content
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print(f"response_2_text: {response_2_text}")
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assert len(response_2_text) < len(response_1_text)
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response_3 = litellm.completion(
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model="luminous-base",
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messages=[{"content": "Hello, how are you?", "role": "user"}],
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n=2,
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)
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assert len(response_3.choices) > 1
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# aleph_alpha_test_completion()
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# Petals - calls are too slow, will cause circle ci to fail due to delay. Test locally.
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# def petals_completion():
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# litellm.PetalsConfig(max_new_tokens=10)
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# # litellm.set_verbose=True
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# try:
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# # OVERRIDE WITH DYNAMIC MAX TOKENS
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# response_1 = litellm.completion(
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# model="petals/petals-team/StableBeluga2",
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# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
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# api_base="https://chat.petals.dev/api/v1/generate",
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# max_tokens=100
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# )
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# response_1_text = response_1.choices[0].message.content
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# print(f"response_1_text: {response_1_text}")
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# # USE CONFIG TOKENS
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# response_2 = litellm.completion(
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# model="petals/petals-team/StableBeluga2",
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# api_base="https://chat.petals.dev/api/v1/generate",
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# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
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# )
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# response_2_text = response_2.choices[0].message.content
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# print(f"response_2_text: {response_2_text}")
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# assert len(response_2_text) < len(response_1_text)
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# petals_completion()
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# VertexAI
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# We don't have vertex ai configured for circle ci yet -- need to figure this out.
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# def vertex_ai_test_completion():
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# litellm.VertexAIConfig(max_output_tokens=10)
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# # litellm.set_verbose=True
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# try:
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# # OVERRIDE WITH DYNAMIC MAX TOKENS
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# response_1 = litellm.completion(
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# model="chat-bison",
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# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
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# max_tokens=100
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# )
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# response_1_text = response_1.choices[0].message.content
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# print(f"response_1_text: {response_1_text}")
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# # USE CONFIG TOKENS
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# response_2 = litellm.completion(
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# model="chat-bison",
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# messages=[{ "content": "Hello, how are you? Be as verbose as possible","role": "user"}],
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# )
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# response_2_text = response_2.choices[0].message.content
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# print(f"response_2_text: {response_2_text}")
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# assert len(response_2_text) < len(response_1_text)
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# except Exception as e:
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# pytest.fail(f"Error occurred: {e}")
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# vertex_ai_test_completion()
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# Sagemaker
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@pytest.mark.skip(reason="AWS Suspended Account")
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def sagemaker_test_completion():
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litellm.SagemakerConfig(max_new_tokens=10)
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# litellm.set_verbose=True
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try:
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# OVERRIDE WITH DYNAMIC MAX TOKENS
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response_1 = litellm.completion(
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model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4",
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messages=[
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{
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"content": "Hello, how are you? Be as verbose as possible",
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"role": "user",
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}
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],
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max_tokens=100,
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)
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response_1_text = response_1.choices[0].message.content
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print(f"response_1_text: {response_1_text}")
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# USE CONFIG TOKENS
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response_2 = litellm.completion(
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model="sagemaker/berri-benchmarking-Llama-2-70b-chat-hf-4",
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messages=[
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{
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"content": "Hello, how are you? Be as verbose as possible",
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"role": "user",
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}
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],
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)
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response_2_text = response_2.choices[0].message.content
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print(f"response_2_text: {response_2_text}")
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assert len(response_2_text) < len(response_1_text)
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except Exception as e:
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pytest.fail(f"Error occurred: {e}")
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# sagemaker_test_completion()
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def test_sagemaker_default_region():
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"""
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If no regions are specified in config or in environment, the default region is us-west-2
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"""
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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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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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response = litellm.completion(
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model="sagemaker/mock-endpoint",
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messages=[{"content": "Hello, world!", "role": "user"}],
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)
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mock_post.assert_called_once()
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_, kwargs = mock_post.call_args
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print(f"kwargs: {kwargs}")
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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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print("url=", kwargs["url"])
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assert (
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kwargs["url"]
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== "https://runtime.sagemaker.us-west-2.amazonaws.com/endpoints/mock-endpoint/invocations"
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)
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# test_sagemaker_default_region()
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def test_sagemaker_environment_region():
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"""
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If a region is specified in the environment, use that region instead of us-west-2
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"""
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expected_region = "us-east-1"
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os.environ["AWS_REGION_NAME"] = expected_region
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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",
|
|
"object": "text_completion",
|
|
"created": 1629800000,
|
|
"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
|
|
"choices": [
|
|
{
|
|
"text": "This is a mock response from SageMaker.",
|
|
"index": 0,
|
|
"logprobs": None,
|
|
"finish_reason": "length",
|
|
}
|
|
],
|
|
"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
|
|
}
|
|
|
|
mock_response.json = return_val
|
|
mock_response.status_code = 200
|
|
|
|
with patch(
|
|
"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
|
|
return_value=mock_response,
|
|
) as mock_post:
|
|
response = litellm.completion(
|
|
model="sagemaker/mock-endpoint",
|
|
messages=[{"content": "Hello, world!", "role": "user"}],
|
|
)
|
|
mock_post.assert_called_once()
|
|
_, kwargs = mock_post.call_args
|
|
args_to_sagemaker = json.loads(kwargs["data"])
|
|
print("Arguments passed to sagemaker=", args_to_sagemaker)
|
|
print("url=", kwargs["url"])
|
|
|
|
assert (
|
|
kwargs["url"]
|
|
== f"https://runtime.sagemaker.{expected_region}.amazonaws.com/endpoints/mock-endpoint/invocations"
|
|
)
|
|
|
|
del os.environ["AWS_REGION_NAME"] # cleanup
|
|
|
|
|
|
# test_sagemaker_environment_region()
|
|
|
|
|
|
def test_sagemaker_config_region():
|
|
"""
|
|
If a region is specified as part of the optional parameters of the completion, including as
|
|
part of the config file, then use that region instead of us-west-2
|
|
"""
|
|
expected_region = "us-east-1"
|
|
mock_response = MagicMock()
|
|
|
|
def return_val():
|
|
return {
|
|
"generated_text": "This is a mock response from SageMaker.",
|
|
"id": "cmpl-mockid",
|
|
"object": "text_completion",
|
|
"created": 1629800000,
|
|
"model": "sagemaker/jumpstart-dft-hf-textgeneration1-mp-20240815-185614",
|
|
"choices": [
|
|
{
|
|
"text": "This is a mock response from SageMaker.",
|
|
"index": 0,
|
|
"logprobs": None,
|
|
"finish_reason": "length",
|
|
}
|
|
],
|
|
"usage": {"prompt_tokens": 1, "completion_tokens": 8, "total_tokens": 9},
|
|
}
|
|
|
|
mock_response.json = return_val
|
|
mock_response.status_code = 200
|
|
|
|
with patch(
|
|
"litellm.llms.custom_httpx.http_handler.HTTPHandler.post",
|
|
return_value=mock_response,
|
|
) as mock_post:
|
|
|
|
response = litellm.completion(
|
|
model="sagemaker/mock-endpoint",
|
|
messages=[{"content": "Hello, world!", "role": "user"}],
|
|
aws_region_name=expected_region,
|
|
)
|
|
|
|
mock_post.assert_called_once()
|
|
_, kwargs = mock_post.call_args
|
|
args_to_sagemaker = json.loads(kwargs["data"])
|
|
print("Arguments passed to sagemaker=", args_to_sagemaker)
|
|
print("url=", kwargs["url"])
|
|
|
|
assert (
|
|
kwargs["url"]
|
|
== f"https://runtime.sagemaker.{expected_region}.amazonaws.com/endpoints/mock-endpoint/invocations"
|
|
)
|
|
|
|
|
|
# test_sagemaker_config_region()
|
|
|
|
|
|
# test_sagemaker_config_and_environment_region()
|
|
|
|
|
|
# Bedrock
|
|
|
|
|
|
def bedrock_test_completion():
|
|
litellm.AmazonCohereConfig(max_tokens=10)
|
|
# litellm.set_verbose=True
|
|
try:
|
|
# OVERRIDE WITH DYNAMIC MAX TOKENS
|
|
response_1 = litellm.completion(
|
|
model="bedrock/cohere.command-r-v1:0",
|
|
messages=[
|
|
{
|
|
"content": "Hello, how are you? Be as verbose as possible",
|
|
"role": "user",
|
|
}
|
|
],
|
|
max_tokens=100,
|
|
)
|
|
response_1_text = response_1.choices[0].message.content
|
|
print(f"response_1_text: {response_1_text}")
|
|
|
|
# USE CONFIG TOKENS
|
|
response_2 = litellm.completion(
|
|
model="bedrock/cohere.command-r-v1:0",
|
|
messages=[
|
|
{
|
|
"content": "Hello, how are you? Be as verbose as possible",
|
|
"role": "user",
|
|
}
|
|
],
|
|
)
|
|
response_2_text = response_2.choices[0].message.content
|
|
print(f"response_2_text: {response_2_text}")
|
|
|
|
assert len(response_2_text) < len(response_1_text)
|
|
except RateLimitError:
|
|
pass
|
|
except Exception as e:
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# bedrock_test_completion()
|
|
|
|
|
|
# OpenAI Chat Completion
|
|
def openai_test_completion():
|
|
litellm.OpenAIConfig(max_tokens=10)
|
|
# litellm.set_verbose=True
|
|
try:
|
|
# OVERRIDE WITH DYNAMIC MAX TOKENS
|
|
response_1 = litellm.completion(
|
|
model="gpt-3.5-turbo",
|
|
messages=[
|
|
{
|
|
"content": "Hello, how are you? Be as verbose as possible",
|
|
"role": "user",
|
|
}
|
|
],
|
|
max_tokens=100,
|
|
)
|
|
response_1_text = response_1.choices[0].message.content
|
|
print(f"response_1_text: {response_1_text}")
|
|
|
|
# USE CONFIG TOKENS
|
|
response_2 = litellm.completion(
|
|
model="gpt-3.5-turbo",
|
|
messages=[
|
|
{
|
|
"content": "Hello, how are you? Be as verbose as possible",
|
|
"role": "user",
|
|
}
|
|
],
|
|
)
|
|
response_2_text = response_2.choices[0].message.content
|
|
print(f"response_2_text: {response_2_text}")
|
|
|
|
assert len(response_2_text) < len(response_1_text)
|
|
except Exception as e:
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# openai_test_completion()
|
|
|
|
|
|
# OpenAI Text Completion
|
|
def openai_text_completion_test():
|
|
litellm.OpenAITextCompletionConfig(max_tokens=10)
|
|
# litellm.set_verbose=True
|
|
try:
|
|
# OVERRIDE WITH DYNAMIC MAX TOKENS
|
|
response_1 = litellm.completion(
|
|
model="gpt-3.5-turbo-instruct",
|
|
messages=[
|
|
{
|
|
"content": "Hello, how are you? Be as verbose as possible",
|
|
"role": "user",
|
|
}
|
|
],
|
|
max_tokens=100,
|
|
)
|
|
response_1_text = response_1.choices[0].message.content
|
|
print(f"response_1_text: {response_1_text}")
|
|
|
|
# USE CONFIG TOKENS
|
|
response_2 = litellm.completion(
|
|
model="gpt-3.5-turbo-instruct",
|
|
messages=[
|
|
{
|
|
"content": "Hello, how are you? Be as verbose as possible",
|
|
"role": "user",
|
|
}
|
|
],
|
|
)
|
|
response_2_text = response_2.choices[0].message.content
|
|
print(f"response_2_text: {response_2_text}")
|
|
|
|
assert len(response_2_text) < len(response_1_text)
|
|
|
|
response_3 = litellm.completion(
|
|
model="gpt-3.5-turbo-instruct",
|
|
messages=[{"content": "Hello, how are you?", "role": "user"}],
|
|
n=2,
|
|
)
|
|
assert len(response_3.choices) > 1
|
|
except Exception as e:
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# openai_text_completion_test()
|
|
|
|
|
|
# Azure OpenAI
|
|
def azure_openai_test_completion():
|
|
litellm.AzureOpenAIConfig(max_tokens=10)
|
|
# litellm.set_verbose=True
|
|
try:
|
|
# OVERRIDE WITH DYNAMIC MAX TOKENS
|
|
response_1 = litellm.completion(
|
|
model="azure/gpt-4.1-mini",
|
|
messages=[
|
|
{
|
|
"content": "Hello, how are you? Be as verbose as possible",
|
|
"role": "user",
|
|
}
|
|
],
|
|
max_tokens=100,
|
|
)
|
|
response_1_text = response_1.choices[0].message.content
|
|
print(f"response_1_text: {response_1_text}")
|
|
|
|
# USE CONFIG TOKENS
|
|
response_2 = litellm.completion(
|
|
model="azure/gpt-4.1-mini",
|
|
messages=[
|
|
{
|
|
"content": "Hello, how are you? Be as verbose as possible",
|
|
"role": "user",
|
|
}
|
|
],
|
|
)
|
|
response_2_text = response_2.choices[0].message.content
|
|
print(f"response_2_text: {response_2_text}")
|
|
|
|
assert len(response_2_text) < len(response_1_text)
|
|
except Exception as e:
|
|
pytest.fail(f"Error occurred: {e}")
|
|
|
|
|
|
# azure_openai_test_completion()
|