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d57be47b0f
* ci(config.yml): add a 'check_code_quality' step Addresses https://github.com/BerriAI/litellm/issues/5991 * ci(config.yml): check why circle ci doesn't pick up this test * ci(config.yml): fix to run 'check_code_quality' tests * fix(__init__.py): fix unprotected import * fix(__init__.py): don't remove unused imports * build(ruff.toml): update ruff.toml to ignore unused imports * fix: fix: ruff + pyright - fix linting + type-checking errors * fix: fix linting errors * fix(lago.py): fix module init error * fix: fix linting errors * ci(config.yml): cd into correct dir for checks * fix(proxy_server.py): fix linting error * fix(utils.py): fix bare except causes ruff linting errors * fix: ruff - fix remaining linting errors * fix(clickhouse.py): use standard logging object * fix(__init__.py): fix unprotected import * fix: ruff - fix linting errors * fix: fix linting errors * ci(config.yml): cleanup code qa step (formatting handled in local_testing) * fix(_health_endpoints.py): fix ruff linting errors * ci(config.yml): just use ruff in check_code_quality pipeline for now * build(custom_guardrail.py): include missing file * style(embedding_handler.py): fix ruff check
1184 lines
44 KiB
Python
1184 lines
44 KiB
Python
## Uses the huggingface text generation inference API
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import copy
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import enum
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import json
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import os
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import time
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import types
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from enum import Enum
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from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union, get_args
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import httpx
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import requests
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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.secret_managers.main import get_secret_str
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from litellm.types.completion import ChatCompletionMessageToolCallParam
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from litellm.utils import Choices, CustomStreamWrapper, Message, ModelResponse, Usage
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from .base import BaseLLM
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from .prompt_templates.factory import custom_prompt, prompt_factory
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class HuggingfaceError(Exception):
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def __init__(
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self,
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status_code,
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message,
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request: Optional[httpx.Request] = None,
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response: Optional[httpx.Response] = None,
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):
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self.status_code = status_code
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self.message = message
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if request is not None:
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self.request = request
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else:
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self.request = httpx.Request(
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method="POST", url="https://api-inference.huggingface.co/models"
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)
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if response is not None:
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self.response = response
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else:
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self.response = httpx.Response(
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status_code=status_code, request=self.request
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)
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super().__init__(
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self.message
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) # Call the base class constructor with the parameters it needs
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hf_task_list = [
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"text-generation-inference",
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"conversational",
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"text-classification",
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"text-generation",
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]
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hf_tasks = Literal[
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"text-generation-inference",
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"conversational",
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"text-classification",
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"text-generation",
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]
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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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class HuggingfaceConfig:
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"""
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Reference: https://huggingface.github.io/text-generation-inference/#/Text%20Generation%20Inference/compat_generate
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"""
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hf_task: Optional[hf_tasks] = (
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None # litellm-specific param, used to know the api spec to use when calling huggingface api
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)
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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] = True # enables returning logprobs + best of
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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] = (
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False # by default don't return the input as part of the output
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)
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seed: Optional[int] = None
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temperature: Optional[float] = None
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top_k: Optional[int] = None
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top_n_tokens: 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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temperature: Optional[float] = None,
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top_k: Optional[int] = None,
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top_n_tokens: 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()
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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 {
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k: v
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for k, v in cls.__dict__.items()
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if not k.startswith("__")
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and not isinstance(
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v,
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(
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types.FunctionType,
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types.BuiltinFunctionType,
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classmethod,
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staticmethod,
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),
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)
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and v is not None
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}
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def get_special_options_params(self):
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return ["use_cache", "wait_for_model"]
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def get_supported_openai_params(self):
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return [
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"stream",
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"temperature",
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"max_tokens",
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"max_completion_tokens",
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"top_p",
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"stop",
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"n",
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"echo",
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]
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def map_openai_params(
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self, non_default_params: dict, optional_params: dict
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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["do_sample"] = (
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True # Need to sample if you want best of for hf inference endpoints
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)
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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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return optional_params
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def get_hf_api_key(self) -> Optional[str]:
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return get_secret_str("HUGGINGFACE_API_KEY")
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def output_parser(generated_text: str):
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"""
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Parse the output text to remove any special characters. In our current approach we just check for ChatML tokens.
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Initial issue that prompted this - https://github.com/BerriAI/litellm/issues/763
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"""
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chat_template_tokens = ["<|assistant|>", "<|system|>", "<|user|>", "<s>", "</s>"]
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for token in chat_template_tokens:
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if generated_text.strip().startswith(token):
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generated_text = generated_text.replace(token, "", 1)
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if generated_text.endswith(token):
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generated_text = generated_text[::-1].replace(token[::-1], "", 1)[::-1]
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return generated_text
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tgi_models_cache = None
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conv_models_cache = None
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def read_tgi_conv_models():
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try:
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global tgi_models_cache, conv_models_cache
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# Check if the cache is already populated
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# so we don't keep on reading txt file if there are 1k requests
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if (tgi_models_cache is not None) and (conv_models_cache is not None):
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return tgi_models_cache, conv_models_cache
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# If not, read the file and populate the cache
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tgi_models = set()
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script_directory = os.path.dirname(os.path.abspath(__file__))
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# Construct the file path relative to the script's directory
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file_path = os.path.join(
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script_directory,
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"huggingface_llms_metadata",
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"hf_text_generation_models.txt",
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)
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with open(file_path, "r") as file:
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for line in file:
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tgi_models.add(line.strip())
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# Cache the set for future use
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tgi_models_cache = tgi_models
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# If not, read the file and populate the cache
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file_path = os.path.join(
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script_directory,
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"huggingface_llms_metadata",
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"hf_conversational_models.txt",
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)
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conv_models = set()
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with open(file_path, "r") as file:
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for line in file:
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conv_models.add(line.strip())
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# Cache the set for future use
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conv_models_cache = conv_models
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return tgi_models, conv_models
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except Exception:
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return set(), set()
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def get_hf_task_for_model(model: str) -> Tuple[hf_tasks, str]:
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# read text file, cast it to set
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# read the file called "huggingface_llms_metadata/hf_text_generation_models.txt"
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if model.split("/")[0] in hf_task_list:
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split_model = model.split("/", 1)
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return split_model[0], split_model[1] # type: ignore
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tgi_models, conversational_models = read_tgi_conv_models()
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if model in tgi_models:
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return "text-generation-inference", model
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elif model in conversational_models:
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return "conversational", model
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elif "roneneldan/TinyStories" in model:
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return "text-generation", model
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else:
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return "text-generation-inference", model # default to tgi
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from litellm.llms.custom_httpx.http_handler import AsyncHTTPHandler, HTTPHandler
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def get_hf_task_embedding_for_model(
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model: str, task_type: Optional[str], api_base: str
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) -> 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=api_base)
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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(
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model: str, task_type: Optional[str], api_base: str
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) -> 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 = AsyncHTTPHandler(concurrent_limit=1)
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model_info = await http_client.get(url=api_base)
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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 Huggingface(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 _validate_environment(self, api_key, headers) -> dict:
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default_headers = {
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"content-type": "application/json",
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}
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if api_key and headers is None:
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default_headers["Authorization"] = (
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f"Bearer {api_key}" # Huggingface Inference Endpoint default is to accept bearer tokens
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)
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headers = default_headers
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elif headers:
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headers = headers
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else:
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headers = default_headers
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return headers
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def convert_to_model_response_object(
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self,
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completion_response,
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model_response: litellm.ModelResponse,
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task: hf_tasks,
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optional_params,
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encoding,
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input_text,
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model,
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):
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if task == "conversational":
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if len(completion_response["generated_text"]) > 0: # type: ignore
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model_response.choices[0].message.content = completion_response[ # type: ignore
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"generated_text"
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]
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elif task == "text-generation-inference":
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if (
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not isinstance(completion_response, list)
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or not isinstance(completion_response[0], dict)
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or "generated_text" not in completion_response[0]
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):
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raise HuggingfaceError(
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status_code=422,
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message=f"response is not in expected format - {completion_response}",
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)
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if len(completion_response[0]["generated_text"]) > 0:
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model_response.choices[0].message.content = output_parser( # type: ignore
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completion_response[0]["generated_text"]
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)
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## GETTING LOGPROBS + FINISH REASON
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if (
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"details" in completion_response[0]
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and "tokens" in completion_response[0]["details"]
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):
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model_response.choices[0].finish_reason = completion_response[0][
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"details"
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]["finish_reason"]
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sum_logprob = 0
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for token in completion_response[0]["details"]["tokens"]:
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if token["logprob"] is not None:
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sum_logprob += token["logprob"]
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setattr(model_response.choices[0].message, "_logprob", sum_logprob) # type: ignore
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if "best_of" in optional_params and optional_params["best_of"] > 1:
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if (
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"details" in completion_response[0]
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and "best_of_sequences" in completion_response[0]["details"]
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):
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choices_list = []
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for idx, item in enumerate(
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completion_response[0]["details"]["best_of_sequences"]
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):
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sum_logprob = 0
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for token in item["tokens"]:
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if token["logprob"] is not None:
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sum_logprob += token["logprob"]
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if len(item["generated_text"]) > 0:
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message_obj = Message(
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content=output_parser(item["generated_text"]),
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logprobs=sum_logprob,
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)
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else:
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message_obj = Message(content=None)
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choice_obj = Choices(
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finish_reason=item["finish_reason"],
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index=idx + 1,
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message=message_obj,
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)
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choices_list.append(choice_obj)
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model_response.choices.extend(choices_list)
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elif task == "text-classification":
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model_response.choices[0].message.content = json.dumps( # type: ignore
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completion_response
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)
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else:
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if len(completion_response[0]["generated_text"]) > 0:
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model_response.choices[0].message.content = output_parser( # type: ignore
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completion_response[0]["generated_text"]
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)
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## CALCULATING USAGE
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prompt_tokens = 0
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try:
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prompt_tokens = len(
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encoding.encode(input_text)
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) ##[TODO] use the llama2 tokenizer here
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except Exception:
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# this should remain non blocking we should not block a response returning if calculating usage fails
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pass
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output_text = model_response["choices"][0]["message"].get("content", "")
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if output_text is not None and len(output_text) > 0:
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completion_tokens = 0
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try:
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completion_tokens = len(
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encoding.encode(
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model_response["choices"][0]["message"].get("content", "")
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)
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) ##[TODO] use the llama2 tokenizer here
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except Exception:
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# this should remain non blocking we should not block a response returning if calculating usage fails
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pass
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else:
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completion_tokens = 0
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|
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model_response.created = int(time.time())
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model_response.model = model
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usage = Usage(
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prompt_tokens=prompt_tokens,
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completion_tokens=completion_tokens,
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total_tokens=prompt_tokens + completion_tokens,
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)
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setattr(model_response, "usage", usage)
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model_response._hidden_params["original_response"] = completion_response
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return model_response
|
|
|
|
def completion(
|
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self,
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model: str,
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messages: list,
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|
api_base: Optional[str],
|
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headers: Optional[dict],
|
|
model_response: ModelResponse,
|
|
print_verbose: Callable,
|
|
timeout: float,
|
|
encoding,
|
|
api_key,
|
|
logging_obj,
|
|
optional_params: dict,
|
|
custom_prompt_dict={},
|
|
acompletion: bool = False,
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|
litellm_params=None,
|
|
logger_fn=None,
|
|
):
|
|
super().completion()
|
|
exception_mapping_worked = False
|
|
try:
|
|
headers = self._validate_environment(api_key, headers)
|
|
task, model = get_hf_task_for_model(model)
|
|
## VALIDATE API FORMAT
|
|
if task is None or not isinstance(task, str) or task not in hf_task_list:
|
|
raise Exception(
|
|
"Invalid hf task - {}. Valid formats - {}.".format(task, hf_tasks)
|
|
)
|
|
|
|
print_verbose(f"{model}, {task}")
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completion_url = ""
|
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input_text = ""
|
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if "https" in model:
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completion_url = model
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elif api_base:
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|
completion_url = api_base
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|
elif "HF_API_BASE" in os.environ:
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|
completion_url = os.getenv("HF_API_BASE", "")
|
|
elif "HUGGINGFACE_API_BASE" in os.environ:
|
|
completion_url = os.getenv("HUGGINGFACE_API_BASE", "")
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|
else:
|
|
completion_url = f"https://api-inference.huggingface.co/models/{model}"
|
|
|
|
## Load Config
|
|
config = litellm.HuggingfaceConfig.get_config()
|
|
for k, v in config.items():
|
|
if (
|
|
k not in optional_params
|
|
): # completion(top_k=3) > huggingfaceConfig(top_k=3) <- allows for dynamic variables to be passed in
|
|
optional_params[k] = v
|
|
|
|
### MAP INPUT PARAMS
|
|
#### HANDLE SPECIAL PARAMS
|
|
special_params = HuggingfaceConfig().get_special_options_params()
|
|
special_params_dict = {}
|
|
# Create a list of keys to pop after iteration
|
|
keys_to_pop = []
|
|
|
|
for k, v in optional_params.items():
|
|
if k in special_params:
|
|
special_params_dict[k] = v
|
|
keys_to_pop.append(k)
|
|
|
|
# Pop the keys from the dictionary after iteration
|
|
for k in keys_to_pop:
|
|
optional_params.pop(k)
|
|
if task == "conversational":
|
|
inference_params = copy.deepcopy(optional_params)
|
|
inference_params.pop("details")
|
|
inference_params.pop("return_full_text")
|
|
past_user_inputs = []
|
|
generated_responses = []
|
|
text = ""
|
|
for message in messages:
|
|
if message["role"] == "user":
|
|
if text != "":
|
|
past_user_inputs.append(text)
|
|
text = message["content"]
|
|
elif message["role"] == "assistant" or message["role"] == "system":
|
|
generated_responses.append(message["content"])
|
|
data = {
|
|
"inputs": {
|
|
"text": text,
|
|
"past_user_inputs": past_user_inputs,
|
|
"generated_responses": generated_responses,
|
|
},
|
|
"parameters": inference_params,
|
|
}
|
|
input_text = "".join(message["content"] for message in messages)
|
|
elif task == "text-generation-inference":
|
|
# always send "details" and "return_full_text" as params
|
|
if model in custom_prompt_dict:
|
|
# check if the model has a registered custom prompt
|
|
model_prompt_details = custom_prompt_dict[model]
|
|
prompt = custom_prompt(
|
|
role_dict=model_prompt_details.get("roles", None),
|
|
initial_prompt_value=model_prompt_details.get(
|
|
"initial_prompt_value", ""
|
|
),
|
|
final_prompt_value=model_prompt_details.get(
|
|
"final_prompt_value", ""
|
|
),
|
|
messages=messages,
|
|
)
|
|
else:
|
|
prompt = prompt_factory(model=model, messages=messages)
|
|
data = {
|
|
"inputs": prompt,
|
|
"parameters": optional_params,
|
|
"stream": ( # type: ignore
|
|
True
|
|
if "stream" in optional_params
|
|
and isinstance(optional_params["stream"], bool)
|
|
and optional_params["stream"] is True # type: ignore
|
|
else False
|
|
),
|
|
}
|
|
input_text = prompt
|
|
else:
|
|
# Non TGI and Conversational llms
|
|
# We need this branch, it removes 'details' and 'return_full_text' from params
|
|
if model in custom_prompt_dict:
|
|
# check if the model has a registered custom prompt
|
|
model_prompt_details = custom_prompt_dict[model]
|
|
prompt = custom_prompt(
|
|
role_dict=model_prompt_details.get("roles", {}),
|
|
initial_prompt_value=model_prompt_details.get(
|
|
"initial_prompt_value", ""
|
|
),
|
|
final_prompt_value=model_prompt_details.get(
|
|
"final_prompt_value", ""
|
|
),
|
|
bos_token=model_prompt_details.get("bos_token", ""),
|
|
eos_token=model_prompt_details.get("eos_token", ""),
|
|
messages=messages,
|
|
)
|
|
else:
|
|
prompt = prompt_factory(model=model, messages=messages)
|
|
inference_params = copy.deepcopy(optional_params)
|
|
inference_params.pop("details")
|
|
inference_params.pop("return_full_text")
|
|
data = {
|
|
"inputs": prompt,
|
|
}
|
|
if task == "text-generation-inference":
|
|
data["parameters"] = inference_params
|
|
data["stream"] = ( # type: ignore
|
|
True # type: ignore
|
|
if "stream" in optional_params
|
|
and optional_params["stream"] is True
|
|
else False
|
|
)
|
|
input_text = prompt
|
|
|
|
### RE-ADD SPECIAL PARAMS
|
|
if len(special_params_dict.keys()) > 0:
|
|
data.update({"options": special_params_dict})
|
|
|
|
## LOGGING
|
|
logging_obj.pre_call(
|
|
input=input_text,
|
|
api_key=api_key,
|
|
additional_args={
|
|
"complete_input_dict": data,
|
|
"task": task,
|
|
"headers": headers,
|
|
"api_base": completion_url,
|
|
"acompletion": acompletion,
|
|
},
|
|
)
|
|
## COMPLETION CALL
|
|
|
|
# SSL certificates (a.k.a CA bundle) used to verify the identity of requested hosts.
|
|
ssl_verify = os.getenv("SSL_VERIFY", litellm.ssl_verify)
|
|
if ssl_verify in ["True", "False"]:
|
|
ssl_verify = bool(ssl_verify)
|
|
|
|
if acompletion is True:
|
|
### ASYNC STREAMING
|
|
if optional_params.get("stream", False):
|
|
return self.async_streaming(logging_obj=logging_obj, api_base=completion_url, data=data, headers=headers, model_response=model_response, model=model, timeout=timeout) # type: ignore
|
|
else:
|
|
### ASYNC COMPLETION
|
|
return self.acompletion(api_base=completion_url, data=data, headers=headers, model_response=model_response, task=task, encoding=encoding, input_text=input_text, model=model, optional_params=optional_params, timeout=timeout) # type: ignore
|
|
### SYNC STREAMING
|
|
if "stream" in optional_params and optional_params["stream"] is True:
|
|
response = requests.post(
|
|
completion_url,
|
|
headers=headers,
|
|
data=json.dumps(data),
|
|
stream=optional_params["stream"],
|
|
verify=ssl_verify,
|
|
)
|
|
return response.iter_lines()
|
|
### SYNC COMPLETION
|
|
else:
|
|
response = requests.post(
|
|
completion_url,
|
|
headers=headers,
|
|
data=json.dumps(data),
|
|
verify=ssl_verify,
|
|
)
|
|
|
|
## Some servers might return streaming responses even though stream was not set to true. (e.g. Baseten)
|
|
is_streamed = False
|
|
if (
|
|
response.__dict__["headers"].get("Content-Type", "")
|
|
== "text/event-stream"
|
|
):
|
|
is_streamed = True
|
|
|
|
# iterate over the complete streamed response, and return the final answer
|
|
if is_streamed:
|
|
streamed_response = CustomStreamWrapper(
|
|
completion_stream=response.iter_lines(),
|
|
model=model,
|
|
custom_llm_provider="huggingface",
|
|
logging_obj=logging_obj,
|
|
)
|
|
content = ""
|
|
for chunk in streamed_response:
|
|
content += chunk["choices"][0]["delta"]["content"]
|
|
completion_response: List[Dict[str, Any]] = [
|
|
{"generated_text": content}
|
|
]
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=input_text,
|
|
api_key=api_key,
|
|
original_response=completion_response,
|
|
additional_args={"complete_input_dict": data, "task": task},
|
|
)
|
|
else:
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=input_text,
|
|
api_key=api_key,
|
|
original_response=response.text,
|
|
additional_args={"complete_input_dict": data, "task": task},
|
|
)
|
|
## RESPONSE OBJECT
|
|
try:
|
|
completion_response = response.json()
|
|
if isinstance(completion_response, dict):
|
|
completion_response = [completion_response]
|
|
except Exception:
|
|
import traceback
|
|
|
|
raise HuggingfaceError(
|
|
message=f"Original Response received: {response.text}; Stacktrace: {traceback.format_exc()}",
|
|
status_code=response.status_code,
|
|
)
|
|
print_verbose(f"response: {completion_response}")
|
|
if (
|
|
isinstance(completion_response, dict)
|
|
and "error" in completion_response
|
|
):
|
|
print_verbose(f"completion error: {completion_response['error']}") # type: ignore
|
|
print_verbose(f"response.status_code: {response.status_code}")
|
|
raise HuggingfaceError(
|
|
message=completion_response["error"], # type: ignore
|
|
status_code=response.status_code,
|
|
)
|
|
return self.convert_to_model_response_object(
|
|
completion_response=completion_response,
|
|
model_response=model_response,
|
|
task=task,
|
|
optional_params=optional_params,
|
|
encoding=encoding,
|
|
input_text=input_text,
|
|
model=model,
|
|
)
|
|
except HuggingfaceError as e:
|
|
exception_mapping_worked = True
|
|
raise e
|
|
except Exception as e:
|
|
if exception_mapping_worked:
|
|
raise e
|
|
else:
|
|
import traceback
|
|
|
|
raise HuggingfaceError(status_code=500, message=traceback.format_exc())
|
|
|
|
async def acompletion(
|
|
self,
|
|
api_base: str,
|
|
data: dict,
|
|
headers: dict,
|
|
model_response: ModelResponse,
|
|
task: hf_tasks,
|
|
encoding: Any,
|
|
input_text: str,
|
|
model: str,
|
|
optional_params: dict,
|
|
timeout: float,
|
|
):
|
|
# SSL certificates (a.k.a CA bundle) used to verify the identity of requested hosts.
|
|
ssl_verify = os.getenv("SSL_VERIFY", litellm.ssl_verify)
|
|
|
|
response = None
|
|
try:
|
|
async with httpx.AsyncClient(timeout=timeout, verify=ssl_verify) as client:
|
|
response = await client.post(url=api_base, json=data, headers=headers)
|
|
response_json = response.json()
|
|
if response.status_code != 200:
|
|
if "error" in response_json:
|
|
raise HuggingfaceError(
|
|
status_code=response.status_code,
|
|
message=response_json["error"],
|
|
request=response.request,
|
|
response=response,
|
|
)
|
|
else:
|
|
raise HuggingfaceError(
|
|
status_code=response.status_code,
|
|
message=response.text,
|
|
request=response.request,
|
|
response=response,
|
|
)
|
|
|
|
## RESPONSE OBJECT
|
|
return self.convert_to_model_response_object(
|
|
completion_response=response_json,
|
|
model_response=model_response,
|
|
task=task,
|
|
encoding=encoding,
|
|
input_text=input_text,
|
|
model=model,
|
|
optional_params=optional_params,
|
|
)
|
|
except Exception as e:
|
|
if isinstance(e, httpx.TimeoutException):
|
|
raise HuggingfaceError(status_code=500, message="Request Timeout Error")
|
|
elif isinstance(e, HuggingfaceError):
|
|
raise e
|
|
elif response is not None and hasattr(response, "text"):
|
|
raise HuggingfaceError(
|
|
status_code=500,
|
|
message=f"{str(e)}\n\nOriginal Response: {response.text}",
|
|
)
|
|
else:
|
|
raise HuggingfaceError(status_code=500, message=f"{str(e)}")
|
|
|
|
async def async_streaming(
|
|
self,
|
|
logging_obj,
|
|
api_base: str,
|
|
data: dict,
|
|
headers: dict,
|
|
model_response: ModelResponse,
|
|
model: str,
|
|
timeout: float,
|
|
):
|
|
# SSL certificates (a.k.a CA bundle) used to verify the identity of requested hosts.
|
|
ssl_verify = os.getenv("SSL_VERIFY", litellm.ssl_verify)
|
|
|
|
async with httpx.AsyncClient(timeout=timeout, verify=ssl_verify) as client:
|
|
response = client.stream(
|
|
"POST", url=f"{api_base}", json=data, headers=headers
|
|
)
|
|
async with response as r:
|
|
if r.status_code != 200:
|
|
text = await r.aread()
|
|
raise HuggingfaceError(
|
|
status_code=r.status_code,
|
|
message=str(text),
|
|
)
|
|
"""
|
|
Check first chunk for error message.
|
|
If error message, raise error.
|
|
If not - add back to stream
|
|
"""
|
|
# Async iterator over the lines in the response body
|
|
response_iterator = r.aiter_lines()
|
|
|
|
# Attempt to get the first line/chunk from the response
|
|
try:
|
|
first_chunk = await response_iterator.__anext__()
|
|
except StopAsyncIteration:
|
|
# Handle the case where there are no lines to read (empty response)
|
|
first_chunk = ""
|
|
|
|
# Check the first chunk for an error message
|
|
if (
|
|
"error" in first_chunk.lower()
|
|
): # Adjust this condition based on how error messages are structured
|
|
raise HuggingfaceError(
|
|
status_code=400,
|
|
message=first_chunk,
|
|
)
|
|
|
|
# Create a new async generator that begins with the first_chunk and includes the remaining items
|
|
async def custom_stream_with_first_chunk():
|
|
yield first_chunk # Yield back the first chunk
|
|
async for (
|
|
chunk
|
|
) in response_iterator: # Continue yielding the rest of the chunks
|
|
yield chunk
|
|
|
|
# Creating a new completion stream that starts with the first chunk
|
|
completion_stream = custom_stream_with_first_chunk()
|
|
|
|
streamwrapper = CustomStreamWrapper(
|
|
completion_stream=completion_stream,
|
|
model=model,
|
|
custom_llm_provider="huggingface",
|
|
logging_obj=logging_obj,
|
|
)
|
|
|
|
async for transformed_chunk in streamwrapper:
|
|
yield transformed_chunk
|
|
|
|
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=embed_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 = HuggingfaceConfig().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=embed_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: litellm.EmbeddingResponse,
|
|
model: str,
|
|
input: List,
|
|
encoding: Any,
|
|
) -> litellm.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,
|
|
"total_tokens": input_tokens,
|
|
}
|
|
),
|
|
)
|
|
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 = AsyncHTTPHandler(concurrent_limit=1)
|
|
|
|
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: litellm.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,
|
|
) -> litellm.EmbeddingResponse:
|
|
super().embedding()
|
|
headers = self._validate_environment(api_key, headers=None)
|
|
# 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://api-inference.huggingface.co/models/{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,
|
|
)
|