chore: initial snapshot for gitea/github upload
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from typing import TYPE_CHECKING, Any, List, Literal, Optional, Union
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from httpx import Headers, Response
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from litellm.constants import DEFAULT_MAX_TOKENS
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from litellm.llms.base_llm.chat.transformation import BaseConfig, BaseLLMException
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from litellm.types.llms.openai import AllMessageValues
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from litellm.types.utils import ModelResponse
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from ..common_utils import PredibaseError
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if TYPE_CHECKING:
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from litellm.litellm_core_utils.litellm_logging import Logging as _LiteLLMLoggingObj
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LiteLLMLoggingObj = _LiteLLMLoggingObj
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else:
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LiteLLMLoggingObj = Any
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class PredibaseConfig(BaseConfig):
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"""
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Reference: https://docs.predibase.com/user-guide/inference/rest_api
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"""
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adapter_id: Optional[str] = None
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adapter_source: Optional[Literal["pbase", "hub", "s3"]] = None
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best_of: Optional[int] = None
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decoder_input_details: Optional[bool] = None
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details: bool = True # enables returning logprobs + best of
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max_new_tokens: int = (
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DEFAULT_MAX_TOKENS # openai default - requests hang if max_new_tokens not given
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)
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repetition_penalty: Optional[float] = None
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return_full_text: Optional[
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bool
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] = False # by default don't return the input as part of the output
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seed: Optional[int] = None
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stop: Optional[List[str]] = None
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temperature: Optional[float] = None
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top_k: Optional[int] = None
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top_p: Optional[int] = None
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truncate: Optional[int] = None
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typical_p: Optional[float] = None
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watermark: Optional[bool] = None
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def __init__(
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self,
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best_of: Optional[int] = None,
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decoder_input_details: Optional[bool] = None,
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details: Optional[bool] = None,
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max_new_tokens: Optional[int] = None,
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repetition_penalty: Optional[float] = None,
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return_full_text: Optional[bool] = None,
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seed: Optional[int] = None,
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stop: Optional[List[str]] = None,
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temperature: Optional[float] = None,
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top_k: Optional[int] = None,
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top_p: Optional[int] = None,
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truncate: Optional[int] = None,
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typical_p: Optional[float] = None,
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watermark: Optional[bool] = None,
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) -> None:
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locals_ = locals().copy()
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for key, value in locals_.items():
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if key != "self" and value is not None:
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setattr(self.__class__, key, value)
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@classmethod
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def get_config(cls):
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return super().get_config()
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def get_supported_openai_params(self, model: str):
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return [
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"stream",
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"temperature",
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"max_completion_tokens",
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"max_tokens",
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"top_p",
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"stop",
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"n",
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"response_format",
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]
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def map_openai_params(
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self,
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non_default_params: dict,
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optional_params: dict,
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model: str,
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drop_params: bool,
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) -> dict:
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for param, value in non_default_params.items():
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# temperature, top_p, n, stream, stop, max_tokens, n, presence_penalty default to None
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if param == "temperature":
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if value == 0.0 or value == 0:
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# hugging face exception raised when temp==0
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# Failed: Error occurred: HuggingfaceException - Input validation error: `temperature` must be strictly positive
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value = 0.01
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optional_params["temperature"] = value
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if param == "top_p":
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optional_params["top_p"] = value
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if param == "n":
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optional_params["best_of"] = value
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optional_params[
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"do_sample"
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] = True # Need to sample if you want best of for hf inference endpoints
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if param == "stream":
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optional_params["stream"] = value
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if param == "stop":
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optional_params["stop"] = value
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if param == "max_tokens" or param == "max_completion_tokens":
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# HF TGI raises the following exception when max_new_tokens==0
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# Failed: Error occurred: HuggingfaceException - Input validation error: `max_new_tokens` must be strictly positive
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if value == 0:
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value = 1
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optional_params["max_new_tokens"] = value
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if param == "echo":
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# https://huggingface.co/docs/huggingface_hub/main/en/package_reference/inference_client#huggingface_hub.InferenceClient.text_generation.decoder_input_details
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# Return the decoder input token logprobs and ids. You must set details=True as well for it to be taken into account. Defaults to False
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optional_params["decoder_input_details"] = True
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if param == "response_format":
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optional_params["response_format"] = value
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return optional_params
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def transform_response(
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self,
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model: str,
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raw_response: Response,
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model_response: ModelResponse,
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logging_obj: LiteLLMLoggingObj,
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request_data: dict,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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encoding: str,
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api_key: Optional[str] = None,
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json_mode: Optional[bool] = None,
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) -> ModelResponse:
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raise NotImplementedError(
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"Predibase transformation currently done in handler.py. Need to migrate to this file."
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)
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def transform_request(
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self,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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headers: dict,
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) -> dict:
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raise NotImplementedError(
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"Predibase transformation currently done in handler.py. Need to migrate to this file."
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)
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def get_error_class(
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self, error_message: str, status_code: int, headers: Union[dict, Headers]
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) -> BaseLLMException:
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return PredibaseError(
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status_code=status_code, message=error_message, headers=headers
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)
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def validate_environment(
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self,
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headers: dict,
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model: str,
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messages: List[AllMessageValues],
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optional_params: dict,
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litellm_params: dict,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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) -> dict:
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if api_key is None:
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raise ValueError(
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"Missing Predibase API Key - A call is being made to predibase but no key is set either in the environment variables or via params"
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)
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default_headers = {
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"content-type": "application/json",
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"Authorization": "Bearer {}".format(api_key),
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}
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if headers is not None and isinstance(headers, dict):
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headers = {**default_headers, **headers}
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return headers
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