mirror of
https://github.com/BerriAI/litellm.git
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271 lines
11 KiB
Python
271 lines
11 KiB
Python
## Uses the huggingface text generation inference API
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import os, copy
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import json
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from enum import Enum
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import requests
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import time
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from typing import Callable
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from litellm.utils import ModelResponse, Choices, Message
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from typing import Optional
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from .prompt_templates.factory import prompt_factory, custom_prompt
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class HuggingfaceError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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self.message = message
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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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def validate_environment(api_key):
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headers = {
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"content-type": "application/json",
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}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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return headers
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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(script_directory, "huggingface_llms_metadata", "hf_text_generation_models.txt")
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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(script_directory, "huggingface_llms_metadata", "hf_conversational_models.txt")
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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:
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return set(), set()
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def get_hf_task_for_model(model):
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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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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"
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elif model in conversational_models:
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return "conversational"
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else:
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return None
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def completion(
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model: str,
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messages: list,
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api_base: Optional[str],
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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api_key,
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logging_obj,
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custom_prompt_dict={},
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optional_params=None,
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litellm_params=None,
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logger_fn=None,
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):
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headers = validate_environment(api_key)
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task = get_hf_task_for_model(model)
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print_verbose(f"{model}, {task}")
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completion_url = ""
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input_text = None
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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", "")
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elif "HUGGINGFACE_API_BASE" in os.environ:
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completion_url = os.getenv("HUGGINGFACE_API_BASE", "")
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else:
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completion_url = f"https://api-inference.huggingface.co/models/{model}"
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### MAP INPUT PARAMS
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if task == "conversational":
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inference_params = copy.deepcopy(optional_params)
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inference_params.pop("details")
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inference_params.pop("return_full_text")
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inference_params.pop("task")
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past_user_inputs = []
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generated_responses = []
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text = ""
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for message in messages:
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if message["role"] == "user":
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if text != "":
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past_user_inputs.append(text)
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text = message["content"]
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elif message["role"] == "assistant" or message["role"] == "system":
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generated_responses.append(message["content"])
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data = {
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"inputs": {
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"text": text,
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"past_user_inputs": past_user_inputs,
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"generated_responses": generated_responses
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},
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"parameters": inference_params
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}
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input_text = "".join(message["content"] for message in messages)
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elif task == "text-generation-inference":
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# always send "details" and "return_full_text" as params
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if model in custom_prompt_dict:
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# check if the model has a registered custom prompt
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model_prompt_details = custom_prompt_dict[model]
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prompt = custom_prompt(
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role_dict=model_prompt_details["roles"],
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initial_prompt_value=model_prompt_details["initial_prompt_value"],
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final_prompt_value=model_prompt_details["final_prompt_value"],
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messages=messages
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)
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else:
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prompt = prompt_factory(model=model, messages=messages)
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if "https://api-inference.huggingface.co/models" in completion_url:
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inference_params = copy.deepcopy(optional_params)
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data = {
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"inputs": prompt,
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"parameters": inference_params,
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"stream": True if "stream" in inference_params and inference_params["stream"] == True else False,
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}
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else:
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data = {
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"inputs": prompt,
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"parameters": optional_params,
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"stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
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}
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input_text = prompt
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else:
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# Non TGI and Conversational llms
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# We need this branch, it removes 'details' and 'return_full_text' from params
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if model in custom_prompt_dict:
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# check if the model has a registered custom prompt
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model_prompt_details = custom_prompt_dict[model]
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prompt = custom_prompt(
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role_dict=model_prompt_details["roles"],
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initial_prompt_value=model_prompt_details["initial_prompt_value"],
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final_prompt_value=model_prompt_details["final_prompt_value"],
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messages=messages
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)
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else:
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prompt = prompt_factory(model=model, messages=messages)
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inference_params = copy.deepcopy(optional_params)
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inference_params.pop("details")
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inference_params.pop("return_full_text")
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inference_params.pop("task")
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data = {
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"inputs": prompt,
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"parameters": inference_params,
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"stream": True if "stream" in optional_params and optional_params["stream"] == True else False,
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}
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input_text = prompt
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## LOGGING
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logging_obj.pre_call(
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input=input_text,
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api_key=api_key,
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additional_args={"complete_input_dict": data, "task": task},
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)
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## COMPLETION CALL
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if "stream" in optional_params and optional_params["stream"] == True:
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response = requests.post(
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completion_url,
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headers=headers,
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data=json.dumps(data),
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stream=optional_params["stream"]
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)
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return response.iter_lines()
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else:
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response = requests.post(
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completion_url,
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headers=headers,
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data=json.dumps(data)
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)
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## LOGGING
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logging_obj.post_call(
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input=input_text,
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api_key=api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data, "task": task},
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)
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## RESPONSE OBJECT
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try:
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completion_response = response.json()
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except:
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raise HuggingfaceError(
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message=response.text, status_code=response.status_code
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)
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print_verbose(f"response: {completion_response}")
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if isinstance(completion_response, dict) and "error" in completion_response:
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print_verbose(f"completion error: {completion_response['error']}")
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print_verbose(f"response.status_code: {response.status_code}")
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raise HuggingfaceError(
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message=completion_response["error"],
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status_code=response.status_code,
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)
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else:
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if task == "conversational":
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model_response["choices"][0]["message"][
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"content"
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] = completion_response["generated_text"]
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elif task == "text-generation-inference":
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model_response["choices"][0]["message"][
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"content"
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] = completion_response[0]["generated_text"]
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## GETTING LOGPROBS + FINISH REASON
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if "details" in completion_response[0] and "tokens" in completion_response[0]["details"]:
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model_response.choices[0].finish_reason = completion_response[0]["details"]["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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sum_logprob += token["logprob"]
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model_response["choices"][0]["message"]["logprobs"] = sum_logprob
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if "best_of" in optional_params and optional_params["best_of"] > 1:
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if "details" in completion_response[0] and "best_of_sequences" in completion_response[0]["details"]:
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choices_list = []
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for idx, item in enumerate(completion_response[0]["details"]["best_of_sequences"]):
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sum_logprob = 0
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for token in item["tokens"]:
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sum_logprob += token["logprob"]
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message_obj = Message(content=item["generated_text"], logprobs=sum_logprob)
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choice_obj = Choices(finish_reason=item["finish_reason"], index=idx+1, message=message_obj)
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choices_list.append(choice_obj)
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model_response["choices"].extend(choices_list)
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else:
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model_response["choices"][0]["message"]["content"] = completion_response[0]["generated_text"]
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## CALCULATING USAGE
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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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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"]["content"])
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) ##[TODO] use the llama2 tokenizer here
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model_response["created"] = time.time()
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model_response["model"] = model
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model_response["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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return model_response
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def embedding():
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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