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fix(utils.py): support deepinfra optional params

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Fixes https://github.com/BerriAI/litellm/issues/3855
This commit is contained in:
Krrish Dholakia 2024-05-27 09:16:39 -07:00
parent a6a84e57ce
commit f0f853b941
3 changed files with 109 additions and 38 deletions
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7
litellm/__init__.py
View file

@ -766,7 +766,12 @@ from .llms.bedrock import (
AmazonMistralConfig,
AmazonBedrockGlobalConfig,
)
from .llms.openai import OpenAIConfig, OpenAITextCompletionConfig, MistralConfig
from .llms.openai import (
OpenAIConfig,
OpenAITextCompletionConfig,
MistralConfig,
DeepInfraConfig,
)
from .llms.azure import AzureOpenAIConfig, AzureOpenAIError
from .llms.watsonx import IBMWatsonXAIConfig
from .main import * # type: ignore

97
litellm/llms/openai.py
View file

@ -157,6 +157,101 @@ class MistralConfig:
)
if param == "seed":
optional_params["extra_body"] = {"random_seed": value}
if param == "response_format":
optional_params["response_format"] = value
return optional_params
class DeepInfraConfig:
"""
Reference: https://deepinfra.com/docs/advanced/openai_api
The class `DeepInfra` provides configuration for the DeepInfra's Chat Completions API interface. Below are the parameters:
"""
frequency_penalty: Optional[int] = None
function_call: Optional[Union[str, dict]] = None
functions: Optional[list] = None
logit_bias: Optional[dict] = None
max_tokens: Optional[int] = None
n: Optional[int] = None
presence_penalty: Optional[int] = None
stop: Optional[Union[str, list]] = None
temperature: Optional[int] = None
top_p: Optional[int] = None
response_format: Optional[dict] = None
tools: Optional[list] = None
tool_choice: Optional[Union[str, dict]] = None
def __init__(
self,
frequency_penalty: Optional[int] = None,
function_call: Optional[Union[str, dict]] = None,
functions: Optional[list] = None,
logit_bias: Optional[dict] = None,
max_tokens: Optional[int] = None,
n: Optional[int] = None,
presence_penalty: Optional[int] = None,
stop: Optional[Union[str, list]] = None,
temperature: Optional[int] = None,
top_p: Optional[int] = None,
response_format: Optional[dict] = None,
tools: Optional[list] = None,
tool_choice: Optional[Union[str, dict]] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():
if key != "self" and value is not None:
setattr(self.__class__, key, value)
@classmethod
def get_config(cls):
return {
k: v
for k, v in cls.__dict__.items()
if not k.startswith("__")
and not isinstance(
v,
(
types.FunctionType,
types.BuiltinFunctionType,
classmethod,
staticmethod,
),
)
and v is not None
}
def get_supported_openai_params(self):
return [
"frequency_penalty",
"function_call",
"functions",
"logit_bias",
"max_tokens",
"n",
"presence_penalty",
"stop",
"temperature",
"top_p",
"response_format",
"tools",
"tool_choice",
]
def map_openai_params(
self, non_default_params: dict, optional_params: dict, model: str
):
supported_openai_params = self.get_supported_openai_params()
for param, value in non_default_params.items():
if (
param == "temperature"
and value == 0
and model == "mistralai/Mistral-7B-Instruct-v0.1"
): # this model does no support temperature == 0
value = 0.0001 # close to 0
if param in supported_openai_params:
optional_params[param] = value
return optional_params
@ -197,6 +292,7 @@ class OpenAIConfig:
stop: Optional[Union[str, list]] = None
temperature: Optional[int] = None
top_p: Optional[int] = None
response_format: Optional[dict] = None
def __init__(
self,
@ -210,6 +306,7 @@ class OpenAIConfig:
stop: Optional[Union[str, list]] = None,
temperature: Optional[int] = None,
top_p: Optional[int] = None,
response_format: Optional[dict] = None,
) -> None:
locals_ = locals().copy()
for key, value in locals_.items():

43
litellm/utils.py
View file

@ -5797,30 +5797,11 @@ def get_optional_params(
model=model, custom_llm_provider=custom_llm_provider
)
_check_valid_arg(supported_params=supported_params)
if temperature is not None:
if (
temperature == 0 and model == "mistralai/Mistral-7B-Instruct-v0.1"
): # this model does no support temperature == 0
temperature = 0.0001 # close to 0
optional_params["temperature"] = temperature
if top_p:
optional_params["top_p"] = top_p
if n:
optional_params["n"] = n
if stream:
optional_params["stream"] = stream
if stop:
optional_params["stop"] = stop
if max_tokens:
optional_params["max_tokens"] = max_tokens
if presence_penalty:
optional_params["presence_penalty"] = presence_penalty
if frequency_penalty:
optional_params["frequency_penalty"] = frequency_penalty
if logit_bias:
optional_params["logit_bias"] = logit_bias
if user:
optional_params["user"] = user
optional_params = litellm.DeepInfraConfig().map_openai_params(
non_default_params=non_default_params,
optional_params=optional_params,
model=model,
)
elif custom_llm_provider == "perplexity":
supported_params = get_supported_openai_params(
model=model, custom_llm_provider=custom_llm_provider
@ -6604,19 +6585,7 @@ def get_supported_openai_params(
elif custom_llm_provider == "petals":
return ["max_tokens", "temperature", "top_p", "stream"]
elif custom_llm_provider == "deepinfra":
return [
"temperature",
"top_p",
"n",
"stream",
"stop",
"max_tokens",
"presence_penalty",
"frequency_penalty",
"logit_bias",
"user",
"response_format",
]
return litellm.DeepInfraConfig().get_supported_openai_params()
elif custom_llm_provider == "perplexity":
return [
"temperature",