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from functools import lru_cache
from typing import Any, Dict, Optional, Union
from huggingface_hub import constants
from huggingface_hub.inference._common import RequestParameters
from huggingface_hub.utils import build_hf_headers, get_token, logging
logger = logging.get_logger(__name__)
# Dev purposes only.
# If you want to try to run inference for a new model locally before it's registered on huggingface.co
# for a given Inference Provider, you can add it to the following dictionary.
HARDCODED_MODEL_ID_MAPPING: Dict[str, Dict[str, str]] = {
# "HF model ID" => "Model ID on Inference Provider's side"
#
# Example:
# "Qwen/Qwen2.5-Coder-32B-Instruct": "Qwen2.5-Coder-32B-Instruct",
"cerebras": {},
"cohere": {},
"fal-ai": {},
"fireworks-ai": {},
"hf-inference": {},
"hyperbolic": {},
"nebius": {},
"replicate": {},
"sambanova": {},
"together": {},
}
def filter_none(d: Dict[str, Any]) -> Dict[str, Any]:
return {k: v for k, v in d.items() if v is not None}
class TaskProviderHelper:
"""Base class for task-specific provider helpers."""
def __init__(self, provider: str, base_url: str, task: str) -> None:
self.provider = provider
self.task = task
self.base_url = base_url
def prepare_request(
self,
*,
inputs: Any,
parameters: Dict[str, Any],
headers: Dict,
model: Optional[str],
api_key: Optional[str],
extra_payload: Optional[Dict[str, Any]] = None,
) -> RequestParameters:
"""
Prepare the request to be sent to the provider.
Each step (api_key, model, headers, url, payload) can be customized in subclasses.
"""
# api_key from user, or local token, or raise error
api_key = self._prepare_api_key(api_key)
# mapped model from HF model ID
mapped_model = self._prepare_mapped_model(model)
# default HF headers + user headers (to customize in subclasses)
headers = self._prepare_headers(headers, api_key)
# routed URL if HF token, or direct URL (to customize in '_prepare_route' in subclasses)
url = self._prepare_url(api_key, mapped_model)
# prepare payload (to customize in subclasses)
payload = self._prepare_payload_as_dict(inputs, parameters, mapped_model=mapped_model)
if payload is not None:
payload = recursive_merge(payload, extra_payload or {})
# body data (to customize in subclasses)
data = self._prepare_payload_as_bytes(inputs, parameters, mapped_model, extra_payload)
# check if both payload and data are set and return
if payload is not None and data is not None:
raise ValueError("Both payload and data cannot be set in the same request.")
if payload is None and data is None:
raise ValueError("Either payload or data must be set in the request.")
return RequestParameters(url=url, task=self.task, model=mapped_model, json=payload, data=data, headers=headers)
def get_response(self, response: Union[bytes, Dict]) -> Any:
"""
Return the response in the expected format.
Override this method in subclasses for customized response handling."""
return response
def _prepare_api_key(self, api_key: Optional[str]) -> str:
"""Return the API key to use for the request.
Usually not overwritten in subclasses."""
if api_key is None:
api_key = get_token()
if api_key is None:
raise ValueError(
f"You must provide an api_key to work with {self.provider} API or log in with `huggingface-cli login`."
)
return api_key
def _prepare_mapped_model(self, model: Optional[str]) -> str:
"""Return the mapped model ID to use for the request.
Usually not overwritten in subclasses."""
if model is None:
raise ValueError(f"Please provide an HF model ID supported by {self.provider}.")
# hardcoded mapping for local testing
if HARDCODED_MODEL_ID_MAPPING.get(self.provider, {}).get(model):
return HARDCODED_MODEL_ID_MAPPING[self.provider][model]
provider_mapping = _fetch_inference_provider_mapping(model).get(self.provider)
if provider_mapping is None:
raise ValueError(f"Model {model} is not supported by provider {self.provider}.")
if provider_mapping.task != self.task:
raise ValueError(
f"Model {model} is not supported for task {self.task} and provider {self.provider}. "
f"Supported task: {provider_mapping.task}."
)
if provider_mapping.status == "staging":
logger.warning(
f"Model {model} is in staging mode for provider {self.provider}. Meant for test purposes only."
)
return provider_mapping.provider_id
def _prepare_headers(self, headers: Dict, api_key: str) -> Dict:
"""Return the headers to use for the request.
Override this method in subclasses for customized headers.
"""
return {**build_hf_headers(token=api_key), **headers}
def _prepare_url(self, api_key: str, mapped_model: str) -> str:
"""Return the URL to use for the request.
Usually not overwritten in subclasses."""
base_url = self._prepare_base_url(api_key)
route = self._prepare_route(mapped_model)
return f"{base_url.rstrip('/')}/{route.lstrip('/')}"
def _prepare_base_url(self, api_key: str) -> str:
"""Return the base URL to use for the request.
Usually not overwritten in subclasses."""
# Route to the proxy if the api_key is a HF TOKEN
if api_key.startswith("hf_"):
logger.info(f"Calling '{self.provider}' provider through Hugging Face router.")
return constants.INFERENCE_PROXY_TEMPLATE.format(provider=self.provider)
else:
logger.info(f"Calling '{self.provider}' provider directly.")
return self.base_url
def _prepare_route(self, mapped_model: str) -> str:
"""Return the route to use for the request.
Override this method in subclasses for customized routes.
"""
return ""
def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
"""Return the payload to use for the request, as a dict.
Override this method in subclasses for customized payloads.
Only one of `_prepare_payload_as_dict` and `_prepare_payload_as_bytes` should return a value.
"""
return None
def _prepare_payload_as_bytes(
self, inputs: Any, parameters: Dict, mapped_model: str, extra_payload: Optional[Dict]
) -> Optional[bytes]:
"""Return the body to use for the request, as bytes.
Override this method in subclasses for customized body data.
Only one of `_prepare_payload_as_dict` and `_prepare_payload_as_bytes` should return a value.
"""
return None
class BaseConversationalTask(TaskProviderHelper):
"""
Base class for conversational (chat completion) tasks.
The schema follows the OpenAI API format defined here: https://platform.openai.com/docs/api-reference/chat
"""
def __init__(self, provider: str, base_url: str):
super().__init__(provider=provider, base_url=base_url, task="conversational")
def _prepare_route(self, mapped_model: str) -> str:
return "/v1/chat/completions"
def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
return {"messages": inputs, **filter_none(parameters), "model": mapped_model}
class BaseTextGenerationTask(TaskProviderHelper):
"""
Base class for text-generation (completion) tasks.
The schema follows the OpenAI API format defined here: https://platform.openai.com/docs/api-reference/completions
"""
def __init__(self, provider: str, base_url: str):
super().__init__(provider=provider, base_url=base_url, task="text-generation")
def _prepare_route(self, mapped_model: str) -> str:
return "/v1/completions"
def _prepare_payload_as_dict(self, inputs: Any, parameters: Dict, mapped_model: str) -> Optional[Dict]:
return {"prompt": inputs, **filter_none(parameters), "model": mapped_model}
@lru_cache(maxsize=None)
def _fetch_inference_provider_mapping(model: str) -> Dict:
"""
Fetch provider mappings for a model from the Hub.
"""
from huggingface_hub.hf_api import HfApi
info = HfApi().model_info(model, expand=["inferenceProviderMapping"])
provider_mapping = info.inference_provider_mapping
if provider_mapping is None:
raise ValueError(f"No provider mapping found for model {model}")
return provider_mapping
def recursive_merge(dict1: Dict, dict2: Dict) -> Dict:
return {
**dict1,
**{
key: recursive_merge(dict1[key], value)
if (key in dict1 and isinstance(dict1[key], dict) and isinstance(value, dict))
else value
for key, value in dict2.items()
},
}
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