mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 18:54:30 +00:00
b5d55688e5
* fix move base_aws_llm * fix import * update enforce llms folder style * move prompt_templates * update prompt_templates location * fix imports * fix imports * fix imports * fix imports * fix checks
454 lines
15 KiB
Python
454 lines
15 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 os
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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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import litellm
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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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if sync_mode is True:
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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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)
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print(resp)
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else:
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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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)
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print(resp)
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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 = kwargs["json"]
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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 = kwargs["json"]
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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 = kwargs["json"]
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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 = kwargs["json"]
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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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