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clean up hugging face completion()
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ishaan-jaff
parent
f0e2922710
commit
a474b89779
3 changed files with 129 additions and 123 deletions
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@ -1,5 +1,6 @@
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## Uses the huggingface text generation inference API
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import os, json
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import os
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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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@ -7,7 +8,6 @@ from typing import Callable
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from litellm.utils import ModelResponse
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from typing import Optional
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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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@ -16,127 +16,129 @@ class HuggingfaceError(Exception):
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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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class HuggingfaceRestAPILLM:
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def __init__(self, encoding, logging_obj, api_key=None) -> None:
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self.encoding = encoding
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self.logging_obj = logging_obj
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self.validate_environment(api_key=api_key)
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def validate_environment(
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self, api_key
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): # set up the environment required to run the model
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self.headers = {
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"content-type": "application/json",
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}
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# get the api key if it exists in the environment or is passed in, but don't require it
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self.api_key = api_key
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if self.api_key != None:
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self.headers["Authorization"] = f"Bearer {self.api_key}"
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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: str,
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model_response: ModelResponse,
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print_verbose: Callable,
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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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): # logic for parsing in - calling - parsing out model completion calls
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completion_url: str = ""
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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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else:
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completion_url = f"https://api-inference.huggingface.co/models/{model}"
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prompt = ""
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if (
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"meta-llama" in model and "chat" in model
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): # use the required special tokens for meta-llama - https://huggingface.co/blog/llama2#how-to-prompt-llama-2
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prompt = "<s>"
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for message in messages:
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if message["role"] == "system":
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prompt += "[INST] <<SYS>>" + message["content"]
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elif message["role"] == "assistant":
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prompt += message["content"] + "</s><s>[INST]"
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elif message["role"] == "user":
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prompt += message["content"] + "[/INST]"
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else:
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for message in messages:
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prompt += f"{message['content']}"
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### MAP INPUT PARAMS
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data = {"inputs": prompt, "parameters": optional_params, "stream": True if "stream" in optional_params and optional_params["stream"] == True else False}
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## LOGGING
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self.logging_obj.pre_call(
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def completion(
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model: str,
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messages: list,
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api_base: 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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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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completion_url = ""
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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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else:
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completion_url = f"https://api-inference.huggingface.co/models/{model}"
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prompt = ""
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if (
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"meta-llama" in model and "chat" in model
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): # use the required special tokens for meta-llama - https://huggingface.co/blog/llama2#how-to-prompt-llama-2
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prompt = "<s>"
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for message in messages:
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if message["role"] == "system":
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prompt += "[INST] <<SYS>>" + message["content"]
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elif message["role"] == "assistant":
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prompt += message["content"] + "</s><s>[INST]"
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elif message["role"] == "user":
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prompt += message["content"] + "[/INST]"
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else:
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for message in messages:
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prompt += f"{message['content']}"
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### MAP INPUT PARAMS
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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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## LOGGING
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logging_obj.pre_call(
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input=prompt,
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api_key=self.api_key,
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api_key=api_key,
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additional_args={"complete_input_dict": data},
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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, headers=self.headers, data=json.dumps(data), stream=optional_params["stream"]
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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=prompt,
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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},
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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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return response.iter_lines()
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else:
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response = requests.post(
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completion_url, headers=self.headers, data=json.dumps(data)
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)
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## LOGGING
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self.logging_obj.post_call(
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input=prompt,
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api_key=self.api_key,
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original_response=response.text,
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additional_args={"complete_input_dict": data},
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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(message=response.text, status_code=response.status_code)
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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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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
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if "details" in completion_response[0] and "tokens" in completion_response[0]["details"]:
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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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## CALCULATING USAGE
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prompt_tokens = len(
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self.encoding.encode(prompt)
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) ##[TODO] use the llama2 tokenizer here
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completion_tokens = len(
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self.encoding.encode(model_response["choices"][0]["message"]["content"])
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) ##[TODO] use the llama2 tokenizer here
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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
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if "details" in completion_response[0] and "tokens" in completion_response[0]["details"]:
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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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## CALCULATING USAGE
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prompt_tokens = len(
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encoding.encode(prompt)
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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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pass
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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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self,
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): # logic for parsing in - calling - parsing out model embedding calls
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pass
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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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