mirror of
https://github.com/BerriAI/litellm.git
synced 2025-04-25 02:34:29 +00:00
34bdf36eab
* 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* fix tests following breaking change in main * use ChatCompletionRequest * fix tests and lint * [Hugging Face] Remove outdated chat completion tests and fix embedding tests (#9749) * remove or fix tests * fix link in doc * fix(config_settings.md): document hf api key ---------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>
421 lines
13 KiB
Python
421 lines
13 KiB
Python
import json
|
|
import os
|
|
from typing import (
|
|
Any,
|
|
Callable,
|
|
Dict,
|
|
List,
|
|
Literal,
|
|
Optional,
|
|
Union,
|
|
get_args,
|
|
)
|
|
|
|
import httpx
|
|
|
|
import litellm
|
|
from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLoggingObj
|
|
from litellm.llms.custom_httpx.http_handler import (
|
|
AsyncHTTPHandler,
|
|
HTTPHandler,
|
|
get_async_httpx_client,
|
|
)
|
|
from litellm.types.utils import EmbeddingResponse
|
|
|
|
from ...base import BaseLLM
|
|
from ..common_utils import HuggingFaceError
|
|
from .transformation import HuggingFaceEmbeddingConfig
|
|
|
|
config = HuggingFaceEmbeddingConfig()
|
|
|
|
HF_HUB_URL = "https://huggingface.co"
|
|
|
|
hf_tasks_embeddings = Literal[ # pipeline tags + hf tei endpoints - https://huggingface.github.io/text-embeddings-inference/#/
|
|
"sentence-similarity", "feature-extraction", "rerank", "embed", "similarity"
|
|
]
|
|
|
|
|
|
|
|
def get_hf_task_embedding_for_model(model: str, task_type: Optional[str], api_base: str) -> Optional[str]:
|
|
if task_type is not None:
|
|
if task_type in get_args(hf_tasks_embeddings):
|
|
return task_type
|
|
else:
|
|
raise Exception(
|
|
"Invalid task_type={}. Expected one of={}".format(
|
|
task_type, hf_tasks_embeddings
|
|
)
|
|
)
|
|
http_client = HTTPHandler(concurrent_limit=1)
|
|
|
|
model_info = http_client.get(url=f"{api_base}/api/models/{model}")
|
|
|
|
model_info_dict = model_info.json()
|
|
|
|
pipeline_tag: Optional[str] = model_info_dict.get("pipeline_tag", None)
|
|
|
|
return pipeline_tag
|
|
|
|
|
|
async def async_get_hf_task_embedding_for_model(model: str, task_type: Optional[str], api_base: str) -> Optional[str]:
|
|
if task_type is not None:
|
|
if task_type in get_args(hf_tasks_embeddings):
|
|
return task_type
|
|
else:
|
|
raise Exception(
|
|
"Invalid task_type={}. Expected one of={}".format(
|
|
task_type, hf_tasks_embeddings
|
|
)
|
|
)
|
|
http_client = get_async_httpx_client(
|
|
llm_provider=litellm.LlmProviders.HUGGINGFACE,
|
|
)
|
|
|
|
model_info = await http_client.get(url=f"{api_base}/api/models/{model}")
|
|
|
|
model_info_dict = model_info.json()
|
|
|
|
pipeline_tag: Optional[str] = model_info_dict.get("pipeline_tag", None)
|
|
|
|
return pipeline_tag
|
|
|
|
|
|
class HuggingFaceEmbedding(BaseLLM):
|
|
_client_session: Optional[httpx.Client] = None
|
|
_aclient_session: Optional[httpx.AsyncClient] = None
|
|
|
|
def __init__(self) -> None:
|
|
super().__init__()
|
|
|
|
def _transform_input_on_pipeline_tag(
|
|
self, input: List, pipeline_tag: Optional[str]
|
|
) -> dict:
|
|
if pipeline_tag is None:
|
|
return {"inputs": input}
|
|
if pipeline_tag == "sentence-similarity" or pipeline_tag == "similarity":
|
|
if len(input) < 2:
|
|
raise HuggingFaceError(
|
|
status_code=400,
|
|
message="sentence-similarity requires 2+ sentences",
|
|
)
|
|
return {"inputs": {"source_sentence": input[0], "sentences": input[1:]}}
|
|
elif pipeline_tag == "rerank":
|
|
if len(input) < 2:
|
|
raise HuggingFaceError(
|
|
status_code=400,
|
|
message="reranker requires 2+ sentences",
|
|
)
|
|
return {"inputs": {"query": input[0], "texts": input[1:]}}
|
|
return {"inputs": input} # default to feature-extraction pipeline tag
|
|
|
|
async def _async_transform_input(
|
|
self,
|
|
model: str,
|
|
task_type: Optional[str],
|
|
embed_url: str,
|
|
input: List,
|
|
optional_params: dict,
|
|
) -> dict:
|
|
hf_task = await async_get_hf_task_embedding_for_model(model=model, task_type=task_type, api_base=HF_HUB_URL)
|
|
|
|
data = self._transform_input_on_pipeline_tag(input=input, pipeline_tag=hf_task)
|
|
|
|
if len(optional_params.keys()) > 0:
|
|
data["options"] = optional_params
|
|
|
|
return data
|
|
|
|
def _process_optional_params(self, data: dict, optional_params: dict) -> dict:
|
|
special_options_keys = config.get_special_options_params()
|
|
special_parameters_keys = [
|
|
"min_length",
|
|
"max_length",
|
|
"top_k",
|
|
"top_p",
|
|
"temperature",
|
|
"repetition_penalty",
|
|
"max_time",
|
|
]
|
|
|
|
for k, v in optional_params.items():
|
|
if k in special_options_keys:
|
|
data.setdefault("options", {})
|
|
data["options"][k] = v
|
|
elif k in special_parameters_keys:
|
|
data.setdefault("parameters", {})
|
|
data["parameters"][k] = v
|
|
else:
|
|
data[k] = v
|
|
|
|
return data
|
|
|
|
def _transform_input(
|
|
self,
|
|
input: List,
|
|
model: str,
|
|
call_type: Literal["sync", "async"],
|
|
optional_params: dict,
|
|
embed_url: str,
|
|
) -> dict:
|
|
data: Dict = {}
|
|
|
|
## TRANSFORMATION ##
|
|
if "sentence-transformers" in model:
|
|
if len(input) == 0:
|
|
raise HuggingFaceError(
|
|
status_code=400,
|
|
message="sentence transformers requires 2+ sentences",
|
|
)
|
|
data = {"inputs": {"source_sentence": input[0], "sentences": input[1:]}}
|
|
else:
|
|
data = {"inputs": input}
|
|
|
|
task_type = optional_params.pop("input_type", None)
|
|
|
|
if call_type == "sync":
|
|
hf_task = get_hf_task_embedding_for_model(model=model, task_type=task_type, api_base=HF_HUB_URL)
|
|
elif call_type == "async":
|
|
return self._async_transform_input(
|
|
model=model, task_type=task_type, embed_url=embed_url, input=input
|
|
) # type: ignore
|
|
|
|
data = self._transform_input_on_pipeline_tag(
|
|
input=input, pipeline_tag=hf_task
|
|
)
|
|
|
|
if len(optional_params.keys()) > 0:
|
|
data = self._process_optional_params(
|
|
data=data, optional_params=optional_params
|
|
)
|
|
|
|
return data
|
|
|
|
def _process_embedding_response(
|
|
self,
|
|
embeddings: dict,
|
|
model_response: EmbeddingResponse,
|
|
model: str,
|
|
input: List,
|
|
encoding: Any,
|
|
) -> EmbeddingResponse:
|
|
output_data = []
|
|
if "similarities" in embeddings:
|
|
for idx, embedding in embeddings["similarities"]:
|
|
output_data.append(
|
|
{
|
|
"object": "embedding",
|
|
"index": idx,
|
|
"embedding": embedding, # flatten list returned from hf
|
|
}
|
|
)
|
|
else:
|
|
for idx, embedding in enumerate(embeddings):
|
|
if isinstance(embedding, float):
|
|
output_data.append(
|
|
{
|
|
"object": "embedding",
|
|
"index": idx,
|
|
"embedding": embedding, # flatten list returned from hf
|
|
}
|
|
)
|
|
elif isinstance(embedding, list) and isinstance(embedding[0], float):
|
|
output_data.append(
|
|
{
|
|
"object": "embedding",
|
|
"index": idx,
|
|
"embedding": embedding, # flatten list returned from hf
|
|
}
|
|
)
|
|
else:
|
|
output_data.append(
|
|
{
|
|
"object": "embedding",
|
|
"index": idx,
|
|
"embedding": embedding[0][
|
|
0
|
|
], # flatten list returned from hf
|
|
}
|
|
)
|
|
model_response.object = "list"
|
|
model_response.data = output_data
|
|
model_response.model = model
|
|
input_tokens = 0
|
|
for text in input:
|
|
input_tokens += len(encoding.encode(text))
|
|
|
|
setattr(
|
|
model_response,
|
|
"usage",
|
|
litellm.Usage(
|
|
prompt_tokens=input_tokens,
|
|
completion_tokens=input_tokens,
|
|
total_tokens=input_tokens,
|
|
prompt_tokens_details=None,
|
|
completion_tokens_details=None,
|
|
),
|
|
)
|
|
return model_response
|
|
|
|
async def aembedding(
|
|
self,
|
|
model: str,
|
|
input: list,
|
|
model_response: litellm.utils.EmbeddingResponse,
|
|
timeout: Union[float, httpx.Timeout],
|
|
logging_obj: LiteLLMLoggingObj,
|
|
optional_params: dict,
|
|
api_base: str,
|
|
api_key: Optional[str],
|
|
headers: dict,
|
|
encoding: Callable,
|
|
client: Optional[AsyncHTTPHandler] = None,
|
|
):
|
|
## TRANSFORMATION ##
|
|
data = self._transform_input(
|
|
input=input,
|
|
model=model,
|
|
call_type="sync",
|
|
optional_params=optional_params,
|
|
embed_url=api_base,
|
|
)
|
|
|
|
## LOGGING
|
|
logging_obj.pre_call(
|
|
input=input,
|
|
api_key=api_key,
|
|
additional_args={
|
|
"complete_input_dict": data,
|
|
"headers": headers,
|
|
"api_base": api_base,
|
|
},
|
|
)
|
|
## COMPLETION CALL
|
|
if client is None:
|
|
client = get_async_httpx_client(
|
|
llm_provider=litellm.LlmProviders.HUGGINGFACE,
|
|
)
|
|
|
|
response = await client.post(api_base, headers=headers, data=json.dumps(data))
|
|
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=input,
|
|
api_key=api_key,
|
|
additional_args={"complete_input_dict": data},
|
|
original_response=response,
|
|
)
|
|
|
|
embeddings = response.json()
|
|
|
|
if "error" in embeddings:
|
|
raise HuggingFaceError(status_code=500, message=embeddings["error"])
|
|
|
|
## PROCESS RESPONSE ##
|
|
return self._process_embedding_response(
|
|
embeddings=embeddings,
|
|
model_response=model_response,
|
|
model=model,
|
|
input=input,
|
|
encoding=encoding,
|
|
)
|
|
|
|
def embedding(
|
|
self,
|
|
model: str,
|
|
input: list,
|
|
model_response: EmbeddingResponse,
|
|
optional_params: dict,
|
|
logging_obj: LiteLLMLoggingObj,
|
|
encoding: Callable,
|
|
api_key: Optional[str] = None,
|
|
api_base: Optional[str] = None,
|
|
timeout: Union[float, httpx.Timeout] = httpx.Timeout(None),
|
|
aembedding: Optional[bool] = None,
|
|
client: Optional[Union[HTTPHandler, AsyncHTTPHandler]] = None,
|
|
headers={},
|
|
) -> EmbeddingResponse:
|
|
super().embedding()
|
|
headers = config.validate_environment(
|
|
api_key=api_key,
|
|
headers=headers,
|
|
model=model,
|
|
optional_params=optional_params,
|
|
messages=[],
|
|
)
|
|
task_type = optional_params.pop("input_type", None)
|
|
task = get_hf_task_embedding_for_model(model=model, task_type=task_type, api_base=HF_HUB_URL)
|
|
# print_verbose(f"{model}, {task}")
|
|
embed_url = ""
|
|
if "https" in model:
|
|
embed_url = model
|
|
elif api_base:
|
|
embed_url = api_base
|
|
elif "HF_API_BASE" in os.environ:
|
|
embed_url = os.getenv("HF_API_BASE", "")
|
|
elif "HUGGINGFACE_API_BASE" in os.environ:
|
|
embed_url = os.getenv("HUGGINGFACE_API_BASE", "")
|
|
else:
|
|
embed_url = f"https://router.huggingface.co/hf-inference/pipeline/{task}/{model}"
|
|
|
|
## ROUTING ##
|
|
if aembedding is True:
|
|
return self.aembedding(
|
|
input=input,
|
|
model_response=model_response,
|
|
timeout=timeout,
|
|
logging_obj=logging_obj,
|
|
headers=headers,
|
|
api_base=embed_url, # type: ignore
|
|
api_key=api_key,
|
|
client=client if isinstance(client, AsyncHTTPHandler) else None,
|
|
model=model,
|
|
optional_params=optional_params,
|
|
encoding=encoding,
|
|
)
|
|
|
|
## TRANSFORMATION ##
|
|
|
|
data = self._transform_input(
|
|
input=input,
|
|
model=model,
|
|
call_type="sync",
|
|
optional_params=optional_params,
|
|
embed_url=embed_url,
|
|
)
|
|
|
|
## LOGGING
|
|
logging_obj.pre_call(
|
|
input=input,
|
|
api_key=api_key,
|
|
additional_args={
|
|
"complete_input_dict": data,
|
|
"headers": headers,
|
|
"api_base": embed_url,
|
|
},
|
|
)
|
|
## COMPLETION CALL
|
|
if client is None or not isinstance(client, HTTPHandler):
|
|
client = HTTPHandler(concurrent_limit=1)
|
|
response = client.post(embed_url, headers=headers, data=json.dumps(data))
|
|
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=input,
|
|
api_key=api_key,
|
|
additional_args={"complete_input_dict": data},
|
|
original_response=response,
|
|
)
|
|
|
|
embeddings = response.json()
|
|
|
|
if "error" in embeddings:
|
|
raise HuggingFaceError(status_code=500, message=embeddings["error"])
|
|
|
|
## PROCESS RESPONSE ##
|
|
return self._process_embedding_response(
|
|
embeddings=embeddings,
|
|
model_response=model_response,
|
|
model=model,
|
|
input=input,
|
|
encoding=encoding,
|
|
)
|