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Merge pull request #4080 from BerriAI/litellm_predibase_exception_mapping

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fix(utils.py): improved predibase exception mapping
This commit is contained in:
Krish Dholakia 2024-06-08 20:27:44 -07:00 • committed by GitHub
commit b4fc4abb76
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8 changed files with 215 additions and 38 deletions
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62
litellm/exceptions.py
View file

@ -20,7 +20,7 @@ class AuthenticationError(openai.AuthenticationError): # type: ignore
message,
llm_provider,
model,
response: httpx.Response,
response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
@ -32,8 +32,14 @@ class AuthenticationError(openai.AuthenticationError): # type: ignore
self.litellm_debug_info = litellm_debug_info
self.max_retries = max_retries
self.num_retries = num_retries
self.response = response or httpx.Response(
status_code=self.status_code,
request=httpx.Request(
method="GET", url="https://litellm.ai"
), # mock request object
)
super().__init__(
self.message, response=response, body=None
self.message, response=self.response, body=None
) # Call the base class constructor with the parameters it needs
def __str__(self):
@ -60,7 +66,7 @@ class NotFoundError(openai.NotFoundError): # type: ignore
message,
model,
llm_provider,
response: httpx.Response,
response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
@ -72,8 +78,14 @@ class NotFoundError(openai.NotFoundError): # type: ignore
self.litellm_debug_info = litellm_debug_info
self.max_retries = max_retries
self.num_retries = num_retries
self.response = response or httpx.Response(
status_code=self.status_code,
request=httpx.Request(
method="GET", url="https://litellm.ai"
), # mock request object
)
super().__init__(
self.message, response=response, body=None
self.message, response=self.response, body=None
) # Call the base class constructor with the parameters it needs
def __str__(self):
@ -262,7 +274,7 @@ class RateLimitError(openai.RateLimitError): # type: ignore
message,
llm_provider,
model,
response: httpx.Response,
response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
@ -274,8 +286,18 @@ class RateLimitError(openai.RateLimitError): # type: ignore
self.litellm_debug_info = litellm_debug_info
self.max_retries = max_retries
self.num_retries = num_retries
if response is None:
self.response = httpx.Response(
status_code=429,
request=httpx.Request(
method="POST",
url=" https://cloud.google.com/vertex-ai/",
),
)
else:
self.response = response
super().__init__(
self.message, response=response, body=None
self.message, response=self.response, body=None
) # Call the base class constructor with the parameters it needs
def __str__(self):
@ -421,7 +443,7 @@ class ServiceUnavailableError(openai.APIStatusError): # type: ignore
message,
llm_provider,
model,
response: httpx.Response,
response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
@ -433,8 +455,18 @@ class ServiceUnavailableError(openai.APIStatusError): # type: ignore
self.litellm_debug_info = litellm_debug_info
self.max_retries = max_retries
self.num_retries = num_retries
if response is None:
self.response = httpx.Response(
status_code=self.status_code,
request=httpx.Request(
method="POST",
url=" https://cloud.google.com/vertex-ai/",
),
)
else:
self.response = response
super().__init__(
self.message, response=response, body=None
self.message, response=self.response, body=None
) # Call the base class constructor with the parameters it needs
def __str__(self):
@ -460,7 +492,7 @@ class InternalServerError(openai.InternalServerError): # type: ignore
message,
llm_provider,
model,
response: httpx.Response,
response: Optional[httpx.Response] = None,
litellm_debug_info: Optional[str] = None,
max_retries: Optional[int] = None,
num_retries: Optional[int] = None,
@ -472,8 +504,18 @@ class InternalServerError(openai.InternalServerError): # type: ignore
self.litellm_debug_info = litellm_debug_info
self.max_retries = max_retries
self.num_retries = num_retries
if response is None:
self.response = httpx.Response(
status_code=self.status_code,
request=httpx.Request(
method="POST",
url=" https://cloud.google.com/vertex-ai/",
),
)
else:
self.response = response
super().__init__(
self.message, response=response, body=None
self.message, response=self.response, body=None
) # Call the base class constructor with the parameters it needs
def __str__(self):

0
litellm/integrations/test_httpx.py Normal file
View file

48
litellm/llms/predibase.py
View file

@ -3,6 +3,7 @@
from functools import partial
import os, types
import traceback
import json
from enum import Enum
import requests, copy # type: ignore
@ -242,12 +243,12 @@ class PredibaseChatCompletion(BaseLLM):
"details" in completion_response
and "tokens" in completion_response["details"]
):
model_response.choices[0].finish_reason = completion_response[
"details"
]["finish_reason"]
model_response.choices[0].finish_reason = map_finish_reason(
completion_response["details"]["finish_reason"]
)
sum_logprob = 0
for token in completion_response["details"]["tokens"]:
if token["logprob"] != None:
if token["logprob"] is not None:
sum_logprob += token["logprob"]
model_response["choices"][0][
"message"
@ -265,7 +266,7 @@ class PredibaseChatCompletion(BaseLLM):
):
sum_logprob = 0
for token in item["tokens"]:
if token["logprob"] != None:
if token["logprob"] is not None:
sum_logprob += token["logprob"]
if len(item["generated_text"]) > 0:
message_obj = Message(
@ -275,7 +276,7 @@ class PredibaseChatCompletion(BaseLLM):
else:
message_obj = Message(content=None)
choice_obj = Choices(
finish_reason=item["finish_reason"],
finish_reason=map_finish_reason(item["finish_reason"]),
index=idx + 1,
message=message_obj,
)
@ -285,10 +286,8 @@ class PredibaseChatCompletion(BaseLLM):
## CALCULATING USAGE
prompt_tokens = 0
try:
prompt_tokens = len(
encoding.encode(model_response["choices"][0]["message"]["content"])
) ##[TODO] use a model-specific tokenizer here
except:
prompt_tokens = litellm.token_counter(messages=messages)
except Exception:
# this should remain non blocking we should not block a response returning if calculating usage fails
pass
output_text = model_response["choices"][0]["message"].get("content", "")
@ -331,6 +330,7 @@ class PredibaseChatCompletion(BaseLLM):
logging_obj,
optional_params: dict,
tenant_id: str,
timeout: Union[float, httpx.Timeout],
acompletion=None,
litellm_params=None,
logger_fn=None,
@ -340,6 +340,7 @@ class PredibaseChatCompletion(BaseLLM):
completion_url = ""
input_text = ""
base_url = "https://serving.app.predibase.com"
if "https" in model:
completion_url = model
elif api_base:
@ -349,7 +350,7 @@ class PredibaseChatCompletion(BaseLLM):
completion_url = f"{base_url}/{tenant_id}/deployments/v2/llms/{model}"
if optional_params.get("stream", False) == True:
if optional_params.get("stream", False) is True:
completion_url += "/generate_stream"
else:
completion_url += "/generate"
@ -393,9 +394,9 @@ class PredibaseChatCompletion(BaseLLM):
},
)
## COMPLETION CALL
if acompletion == True:
if acompletion is True:
### ASYNC STREAMING
if stream == True:
if stream is True:
return self.async_streaming(
model=model,
messages=messages,
@ -410,6 +411,7 @@ class PredibaseChatCompletion(BaseLLM):
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=headers,
timeout=timeout,
) # type: ignore
else:
### ASYNC COMPLETION
@ -428,10 +430,11 @@ class PredibaseChatCompletion(BaseLLM):
litellm_params=litellm_params,
logger_fn=logger_fn,
headers=headers,
timeout=timeout,
) # type: ignore
### SYNC STREAMING
if stream == True:
if stream is True:
response = requests.post(
completion_url,
headers=headers,
@ -452,7 +455,6 @@ class PredibaseChatCompletion(BaseLLM):
headers=headers,
data=json.dumps(data),
)
return self.process_response(
model=model,
response=response,
@ -480,23 +482,26 @@ class PredibaseChatCompletion(BaseLLM):
stream,
data: dict,
optional_params: dict,
timeout: Union[float, httpx.Timeout],
litellm_params=None,
logger_fn=None,
headers={},
) -> ModelResponse:
self.async_handler = AsyncHTTPHandler(
timeout=httpx.Timeout(timeout=600.0, connect=5.0)
)
async_handler = AsyncHTTPHandler(timeout=httpx.Timeout(timeout=timeout))
try:
response = await self.async_handler.post(
response = await async_handler.post(
api_base, headers=headers, data=json.dumps(data)
)
except httpx.HTTPStatusError as e:
raise PredibaseError(
status_code=e.response.status_code, message=e.response.text
status_code=e.response.status_code,
message="HTTPStatusError - {}".format(e.response.text),
)
except Exception as e:
raise PredibaseError(status_code=500, message=str(e))
raise PredibaseError(
status_code=500, message="{}\n{}".format(str(e), traceback.format_exc())
)
return self.process_response(
model=model,
response=response,
@ -522,6 +527,7 @@ class PredibaseChatCompletion(BaseLLM):
api_key,
logging_obj,
data: dict,
timeout: Union[float, httpx.Timeout],
optional_params=None,
litellm_params=None,
logger_fn=None,

14
litellm/main.py
View file

@ -432,9 +432,9 @@ def mock_completion(
if isinstance(mock_response, openai.APIError):
raise mock_response
raise litellm.APIError(
status_code=500, # type: ignore
message=str(mock_response),
llm_provider="openai", # type: ignore
status_code=getattr(mock_response, "status_code", 500), # type: ignore
message=getattr(mock_response, "text", str(mock_response)),
llm_provider=getattr(mock_response, "llm_provider", "openai"), # type: ignore
model=model, # type: ignore
request=httpx.Request(method="POST", url="https://api.openai.com/v1/"),
)
@ -1949,7 +1949,8 @@ def completion(
)
api_base = (
optional_params.pop("api_base", None)
api_base
or optional_params.pop("api_base", None)
or optional_params.pop("base_url", None)
or litellm.api_base
or get_secret("PREDIBASE_API_BASE")
@ -1977,12 +1978,13 @@ def completion(
custom_prompt_dict=custom_prompt_dict,
api_key=api_key,
tenant_id=tenant_id,
timeout=timeout,
)
if (
"stream" in optional_params
and optional_params["stream"] == True
and acompletion == False
and optional_params["stream"] is True
and acompletion is False
):
return _model_response
response = _model_response

12
litellm/proxy/_super_secret_config.yaml
View file

@ -8,6 +8,17 @@ model_list:
- model_name: llama3-70b-8192
litellm_params:
model: groq/llama3-70b-8192
- model_name: fake-openai-endpoint
litellm_params:
model: predibase/llama-3-8b-instruct
api_base: "http://0.0.0.0:8081"
api_key: os.environ/PREDIBASE_API_KEY
tenant_id: os.environ/PREDIBASE_TENANT_ID
max_retries: 0
temperature: 0.1
max_new_tokens: 256
return_full_text: false
# - litellm_params:
# api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
# api_key: os.environ/AZURE_EUROPE_API_KEY
@ -56,6 +67,7 @@ router_settings:
litellm_settings:
success_callback: ["langfuse"]
failure_callback: ["langfuse"]
# general_settings:
# alerting: ["email"]

46
litellm/tests/test_exceptions.py
View file

@ -3,6 +3,7 @@ import os
import sys
import traceback
import subprocess, asyncio
from typing import Any
sys.path.insert(
0, os.path.abspath("../..")
@ -19,6 +20,7 @@ from litellm import (
)
from concurrent.futures import ThreadPoolExecutor
import pytest
from unittest.mock import patch, MagicMock
litellm.vertex_project = "pathrise-convert-1606954137718"
litellm.vertex_location = "us-central1"
@ -655,3 +657,47 @@ def test_litellm_predibase_exception():
# accuracy_score = counts[True]/(counts[True] + counts[False])
# print(f"accuracy_score: {accuracy_score}")
@pytest.mark.parametrize("provider", ["predibase"])
def test_exception_mapping(provider):
"""
For predibase, run through a set of mock exceptions
assert that they are being mapped correctly
"""
litellm.set_verbose = True
error_map = {
400: litellm.BadRequestError,
401: litellm.AuthenticationError,
404: litellm.NotFoundError,
408: litellm.Timeout,
429: litellm.RateLimitError,
500: litellm.InternalServerError,
503: litellm.ServiceUnavailableError,
}
for code, expected_exception in error_map.items():
mock_response = Exception()
setattr(mock_response, "text", "This is an error message")
setattr(mock_response, "llm_provider", provider)
setattr(mock_response, "status_code", code)
response: Any = None
try:
response = completion(
model="{}/test-model".format(provider),
messages=[{"role": "user", "content": "Hey, how's it going?"}],
mock_response=mock_response,
)
except expected_exception:
continue
except Exception as e:
response = "{}\n{}".format(str(e), traceback.format_exc())
pytest.fail(
"Did not raise expected exception. Expected={}, Return={},".format(
expected_exception, response
)
)
pass

69
litellm/utils.py
View file

@ -8725,6 +8725,75 @@ def exception_type(
response=original_exception.response,
litellm_debug_info=extra_information,
)
elif hasattr(original_exception, "status_code"):
if original_exception.status_code == 500:
exception_mapping_worked = True
raise litellm.InternalServerError(
message=f"PredibaseException - {original_exception.message}",
llm_provider="predibase",
model=model,
)
elif original_exception.status_code == 401:
exception_mapping_worked = True
raise AuthenticationError(
message=f"PredibaseException - {original_exception.message}",
llm_provider="predibase",
model=model,
)
elif original_exception.status_code == 400:
exception_mapping_worked = True
raise BadRequestError(
message=f"PredibaseException - {original_exception.message}",
llm_provider="predibase",
model=model,
)
elif original_exception.status_code == 404:
exception_mapping_worked = True
raise NotFoundError(
message=f"PredibaseException - {original_exception.message}",
llm_provider="predibase",
model=model,
)
elif original_exception.status_code == 408:
exception_mapping_worked = True
raise Timeout(
message=f"PredibaseException - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
)
elif original_exception.status_code == 422:
exception_mapping_worked = True
raise BadRequestError(
message=f"PredibaseException - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
)
elif original_exception.status_code == 429:
exception_mapping_worked = True
raise RateLimitError(
message=f"PredibaseException - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
)
elif original_exception.status_code == 503:
exception_mapping_worked = True
raise ServiceUnavailableError(
message=f"PredibaseException - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
)
elif original_exception.status_code == 504: # gateway timeout error
exception_mapping_worked = True
raise Timeout(
message=f"PredibaseException - {original_exception.message}",
model=model,
llm_provider=custom_llm_provider,
litellm_debug_info=extra_information,
)
elif custom_llm_provider == "bedrock":
if (
"too many tokens" in error_str

2
ruff.toml
View file

@ -1,3 +1,3 @@
ignore = ["F405"]
ignore = ["F405", "E402"]
extend-select = ["E501"]
line-length = 120