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* Add inference providers support for Hugging Face (#8258) * add first version of inference providers for huggingface * temporarily skipping tests * Add documentation * Fix titles * remove max_retries from params and clean up * add suggestions * use llm http handler * update doc * add suggestions * run formatters * add tests * revert * revert * rename file * set maxsize for lru cache * fix embeddings * fix inference url * fix tests following breaking change in main * use ChatCompletionRequest * fix tests and lint * [Hugging Face] Remove outdated chat completion tests and fix embedding tests (#9749) * remove or fix tests * fix link in doc * fix(config_settings.md): document hf api key --------- Co-authored-by: célina <hanouticelina@gmail.com>
This commit is contained in:
Krish Dholakia
GitHub
parent
0d503ad8ad
commit
34bdf36eab
24 changed files with 2052 additions and 2456 deletions
421
litellm/llms/huggingface/embedding/handler.py
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421
litellm/llms/huggingface/embedding/handler.py
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@ -0,0 +1,421 @@
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import json
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import os
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from typing import (
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Any,
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Callable,
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Dict,
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List,
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Literal,
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Optional,
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Union,
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get_args,
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)
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import httpx
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import litellm
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from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
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from litellm.llms.custom_httpx.http_handler import (
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AsyncHTTPHandler,
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HTTPHandler,
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get_async_httpx_client,
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)
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from litellm.types.utils import EmbeddingResponse
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from ...base import BaseLLM
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from ..common_utils import HuggingFaceError
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from .transformation import HuggingFaceEmbeddingConfig
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config = HuggingFaceEmbeddingConfig()
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HF_HUB_URL = "https://huggingface.co"
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hf_tasks_embeddings = Literal[ # pipeline tags + hf tei endpoints - https://huggingface.github.io/text-embeddings-inference/#/
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"sentence-similarity", "feature-extraction", "rerank", "embed", "similarity"
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]
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def get_hf_task_embedding_for_model(model: str, task_type: Optional[str], api_base: str) -> Optional[str]:
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if task_type is not None:
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if task_type in get_args(hf_tasks_embeddings):
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return task_type
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else:
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raise Exception(
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"Invalid task_type={}. Expected one of={}".format(
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task_type, hf_tasks_embeddings
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)
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)
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http_client = HTTPHandler(concurrent_limit=1)
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model_info = http_client.get(url=f"{api_base}/api/models/{model}")
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model_info_dict = model_info.json()
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pipeline_tag: Optional[str] = model_info_dict.get("pipeline_tag", None)
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return pipeline_tag
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async def async_get_hf_task_embedding_for_model(model: str, task_type: Optional[str], api_base: str) -> Optional[str]:
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if task_type is not None:
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if task_type in get_args(hf_tasks_embeddings):
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return task_type
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else:
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raise Exception(
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"Invalid task_type={}. Expected one of={}".format(
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task_type, hf_tasks_embeddings
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)
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)
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http_client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.HUGGINGFACE,
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)
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model_info = await http_client.get(url=f"{api_base}/api/models/{model}")
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model_info_dict = model_info.json()
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pipeline_tag: Optional[str] = model_info_dict.get("pipeline_tag", None)
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return pipeline_tag
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class HuggingFaceEmbedding(BaseLLM):
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_client_session: Optional[httpx.Client] = None
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_aclient_session: Optional[httpx.AsyncClient] = None
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def __init__(self) -> None:
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super().__init__()
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def _transform_input_on_pipeline_tag(
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self, input: List, pipeline_tag: Optional[str]
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) -> dict:
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if pipeline_tag is None:
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return {"inputs": input}
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if pipeline_tag == "sentence-similarity" or pipeline_tag == "similarity":
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if len(input) < 2:
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raise HuggingFaceError(
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status_code=400,
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message="sentence-similarity requires 2+ sentences",
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)
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return {"inputs": {"source_sentence": input[0], "sentences": input[1:]}}
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elif pipeline_tag == "rerank":
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if len(input) < 2:
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raise HuggingFaceError(
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status_code=400,
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message="reranker requires 2+ sentences",
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)
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return {"inputs": {"query": input[0], "texts": input[1:]}}
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return {"inputs": input} # default to feature-extraction pipeline tag
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async def _async_transform_input(
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self,
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model: str,
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task_type: Optional[str],
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embed_url: str,
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input: List,
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optional_params: dict,
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) -> dict:
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hf_task = await async_get_hf_task_embedding_for_model(model=model, task_type=task_type, api_base=HF_HUB_URL)
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data = self._transform_input_on_pipeline_tag(input=input, pipeline_tag=hf_task)
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if len(optional_params.keys()) > 0:
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data["options"] = optional_params
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return data
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def _process_optional_params(self, data: dict, optional_params: dict) -> dict:
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special_options_keys = config.get_special_options_params()
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special_parameters_keys = [
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"min_length",
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"max_length",
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"top_k",
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"top_p",
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"temperature",
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"repetition_penalty",
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"max_time",
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]
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for k, v in optional_params.items():
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if k in special_options_keys:
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data.setdefault("options", {})
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data["options"][k] = v
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elif k in special_parameters_keys:
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data.setdefault("parameters", {})
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data["parameters"][k] = v
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else:
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data[k] = v
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return data
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def _transform_input(
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self,
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input: List,
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model: str,
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call_type: Literal["sync", "async"],
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optional_params: dict,
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embed_url: str,
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) -> dict:
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data: Dict = {}
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## TRANSFORMATION ##
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if "sentence-transformers" in model:
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if len(input) == 0:
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raise HuggingFaceError(
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status_code=400,
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message="sentence transformers requires 2+ sentences",
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)
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data = {"inputs": {"source_sentence": input[0], "sentences": input[1:]}}
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else:
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data = {"inputs": input}
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task_type = optional_params.pop("input_type", None)
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if call_type == "sync":
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hf_task = get_hf_task_embedding_for_model(model=model, task_type=task_type, api_base=HF_HUB_URL)
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elif call_type == "async":
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return self._async_transform_input(
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model=model, task_type=task_type, embed_url=embed_url, input=input
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) # type: ignore
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data = self._transform_input_on_pipeline_tag(
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input=input, pipeline_tag=hf_task
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)
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if len(optional_params.keys()) > 0:
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data = self._process_optional_params(
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data=data, optional_params=optional_params
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)
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return data
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def _process_embedding_response(
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self,
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embeddings: dict,
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model_response: EmbeddingResponse,
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model: str,
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input: List,
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encoding: Any,
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) -> EmbeddingResponse:
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output_data = []
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if "similarities" in embeddings:
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for idx, embedding in embeddings["similarities"]:
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output_data.append(
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{
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"object": "embedding",
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"index": idx,
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"embedding": embedding, # flatten list returned from hf
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}
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)
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else:
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for idx, embedding in enumerate(embeddings):
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if isinstance(embedding, float):
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output_data.append(
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{
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"object": "embedding",
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"index": idx,
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"embedding": embedding, # flatten list returned from hf
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}
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)
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elif isinstance(embedding, list) and isinstance(embedding[0], float):
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output_data.append(
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{
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"object": "embedding",
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"index": idx,
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"embedding": embedding, # flatten list returned from hf
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}
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)
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else:
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output_data.append(
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{
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"object": "embedding",
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"index": idx,
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"embedding": embedding[0][
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0
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], # flatten list returned from hf
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}
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)
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model_response.object = "list"
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model_response.data = output_data
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model_response.model = model
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input_tokens = 0
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for text in input:
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input_tokens += len(encoding.encode(text))
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setattr(
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model_response,
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"usage",
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litellm.Usage(
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prompt_tokens=input_tokens,
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completion_tokens=input_tokens,
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total_tokens=input_tokens,
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prompt_tokens_details=None,
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completion_tokens_details=None,
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),
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)
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return model_response
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async def aembedding(
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self,
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model: str,
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input: list,
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model_response: litellm.utils.EmbeddingResponse,
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timeout: Union[float, httpx.Timeout],
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logging_obj: LiteLLMLoggingObj,
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optional_params: dict,
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api_base: str,
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api_key: Optional[str],
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headers: dict,
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encoding: Callable,
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client: Optional[AsyncHTTPHandler] = None,
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):
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## TRANSFORMATION ##
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data = self._transform_input(
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input=input,
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model=model,
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call_type="sync",
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optional_params=optional_params,
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embed_url=api_base,
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)
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## LOGGING
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logging_obj.pre_call(
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input=input,
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api_key=api_key,
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additional_args={
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"complete_input_dict": data,
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"headers": headers,
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"api_base": api_base,
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},
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)
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## COMPLETION CALL
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if client is None:
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client = get_async_httpx_client(
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llm_provider=litellm.LlmProviders.HUGGINGFACE,
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)
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response = await client.post(api_base, headers=headers, data=json.dumps(data))
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## LOGGING
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logging_obj.post_call(
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input=input,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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original_response=response,
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)
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embeddings = response.json()
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if "error" in embeddings:
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raise HuggingFaceError(status_code=500, message=embeddings["error"])
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## PROCESS RESPONSE ##
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return self._process_embedding_response(
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embeddings=embeddings,
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model_response=model_response,
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model=model,
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input=input,
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encoding=encoding,
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)
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def embedding(
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self,
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model: str,
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input: list,
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model_response: EmbeddingResponse,
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optional_params: dict,
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logging_obj: LiteLLMLoggingObj,
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encoding: Callable,
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api_key: Optional[str] = None,
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api_base: Optional[str] = None,
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timeout: Union[float, httpx.Timeout] = httpx.Timeout(None),
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aembedding: Optional[bool] = None,
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client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
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headers={},
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) -> EmbeddingResponse:
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super().embedding()
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headers = config.validate_environment(
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api_key=api_key,
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headers=headers,
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model=model,
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optional_params=optional_params,
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messages=[],
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)
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task_type = optional_params.pop("input_type", None)
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task = get_hf_task_embedding_for_model(model=model, task_type=task_type, api_base=HF_HUB_URL)
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# print_verbose(f"{model}, {task}")
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embed_url = ""
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if "https" in model:
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embed_url = model
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elif api_base:
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embed_url = api_base
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elif "HF_API_BASE" in os.environ:
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embed_url = os.getenv("HF_API_BASE", "")
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elif "HUGGINGFACE_API_BASE" in os.environ:
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embed_url = os.getenv("HUGGINGFACE_API_BASE", "")
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else:
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embed_url = f"https://router.huggingface.co/hf-inference/pipeline/{task}/{model}"
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## ROUTING ##
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if aembedding is True:
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return self.aembedding(
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input=input,
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model_response=model_response,
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timeout=timeout,
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logging_obj=logging_obj,
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headers=headers,
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api_base=embed_url, # type: ignore
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api_key=api_key,
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client=client if isinstance(client, AsyncHTTPHandler) else None,
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model=model,
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optional_params=optional_params,
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encoding=encoding,
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)
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## TRANSFORMATION ##
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data = self._transform_input(
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input=input,
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model=model,
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call_type="sync",
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optional_params=optional_params,
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embed_url=embed_url,
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)
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## LOGGING
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logging_obj.pre_call(
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input=input,
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api_key=api_key,
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additional_args={
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"complete_input_dict": data,
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"headers": headers,
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"api_base": embed_url,
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},
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)
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## COMPLETION CALL
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if client is None or not isinstance(client, HTTPHandler):
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client = HTTPHandler(concurrent_limit=1)
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response = client.post(embed_url, headers=headers, data=json.dumps(data))
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## LOGGING
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logging_obj.post_call(
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input=input,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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original_response=response,
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)
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embeddings = response.json()
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if "error" in embeddings:
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raise HuggingFaceError(status_code=500, message=embeddings["error"])
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## PROCESS RESPONSE ##
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return self._process_embedding_response(
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embeddings=embeddings,
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model_response=model_response,
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model=model,
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input=input,
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encoding=encoding,
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)
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