mirror of
https://github.com/BerriAI/litellm.git
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755 lines
27 KiB
Python
755 lines
27 KiB
Python
import os, types, traceback
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from enum import Enum
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import json
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import requests # type: ignore
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import time
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from typing import Callable, Optional, Any
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import litellm
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from litellm.utils import ModelResponse, EmbeddingResponse, get_secret, Usage
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import sys
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from copy import deepcopy
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import httpx # type: ignore
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from .prompt_templates.factory import prompt_factory, custom_prompt
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class SagemakerError(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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self.request = httpx.Request(
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method="POST", url="https://us-west-2.console.aws.amazon.com/sagemaker"
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)
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self.response = httpx.Response(status_code=status_code, request=self.request)
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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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import io
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import json
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class TokenIterator:
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def __init__(self, stream, acompletion: bool = False):
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if acompletion == False:
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self.byte_iterator = iter(stream)
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elif acompletion == True:
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self.byte_iterator = stream
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self.buffer = io.BytesIO()
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self.read_pos = 0
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self.end_of_data = False
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def __iter__(self):
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return self
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def __next__(self):
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try:
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while True:
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self.buffer.seek(self.read_pos)
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line = self.buffer.readline()
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if line and line[-1] == ord("\n"):
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response_obj = {"text": "", "is_finished": False}
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self.read_pos += len(line) + 1
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full_line = line[:-1].decode("utf-8")
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line_data = json.loads(full_line.lstrip("data:").rstrip("/n"))
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if line_data.get("generated_text", None) is not None:
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self.end_of_data = True
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response_obj["is_finished"] = True
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response_obj["text"] = line_data["token"]["text"]
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return response_obj
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chunk = next(self.byte_iterator)
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self.buffer.seek(0, io.SEEK_END)
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self.buffer.write(chunk["PayloadPart"]["Bytes"])
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except StopIteration as e:
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if self.end_of_data == True:
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raise e # Re-raise StopIteration
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else:
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self.end_of_data = True
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return "data: [DONE]"
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def __aiter__(self):
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return self
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async def __anext__(self):
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try:
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while True:
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self.buffer.seek(self.read_pos)
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line = self.buffer.readline()
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if line and line[-1] == ord("\n"):
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response_obj = {"text": "", "is_finished": False}
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self.read_pos += len(line) + 1
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full_line = line[:-1].decode("utf-8")
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line_data = json.loads(full_line.lstrip("data:").rstrip("/n"))
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if line_data.get("generated_text", None) is not None:
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self.end_of_data = True
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response_obj["is_finished"] = True
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response_obj["text"] = line_data["token"]["text"]
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return response_obj
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chunk = await self.byte_iterator.__anext__()
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self.buffer.seek(0, io.SEEK_END)
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self.buffer.write(chunk["PayloadPart"]["Bytes"])
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except StopAsyncIteration as e:
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if self.end_of_data == True:
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raise e # Re-raise StopIteration
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else:
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self.end_of_data = True
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return "data: [DONE]"
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class SagemakerConfig:
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"""
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Reference: https://d-uuwbxj1u4cnu.studio.us-west-2.sagemaker.aws/jupyter/default/lab/workspaces/auto-q/tree/DemoNotebooks/meta-textgeneration-llama-2-7b-SDK_1.ipynb
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"""
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max_new_tokens: Optional[int] = None
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top_p: Optional[float] = None
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temperature: Optional[float] = None
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return_full_text: Optional[bool] = None
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def __init__(
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self,
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max_new_tokens: Optional[int] = None,
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top_p: Optional[float] = None,
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temperature: Optional[float] = None,
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return_full_text: 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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"""
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SAGEMAKER AUTH Keys/Vars
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os.environ['AWS_ACCESS_KEY_ID'] = ""
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os.environ['AWS_SECRET_ACCESS_KEY'] = ""
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"""
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# set os.environ['AWS_REGION_NAME'] = <your-region_name>
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def completion(
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model: str,
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messages: list,
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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logging_obj,
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custom_prompt_dict={},
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hf_model_name=None,
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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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acompletion: bool = False,
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):
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import boto3
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# pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
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aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
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aws_access_key_id = optional_params.pop("aws_access_key_id", None)
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aws_region_name = optional_params.pop("aws_region_name", None)
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model_id = optional_params.pop("model_id", None)
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if aws_access_key_id != None:
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# uses auth params passed to completion
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# aws_access_key_id is not None, assume user is trying to auth using litellm.completion
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client = boto3.client(
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service_name="sagemaker-runtime",
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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region_name=aws_region_name,
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)
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else:
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# aws_access_key_id is None, assume user is trying to auth using env variables
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# boto3 automaticaly reads env variables
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# we need to read region name from env
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# I assume majority of users use .env for auth
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region_name = (
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get_secret("AWS_REGION_NAME")
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or "us-west-2" # default to us-west-2 if user not specified
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)
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client = boto3.client(
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service_name="sagemaker-runtime",
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region_name=region_name,
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)
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# pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
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inference_params = deepcopy(optional_params)
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## Load Config
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config = litellm.SagemakerConfig.get_config()
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for k, v in config.items():
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if (
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k not in inference_params
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): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
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inference_params[k] = v
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model = model
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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.get("roles", None),
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initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
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final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
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messages=messages,
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)
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elif hf_model_name in custom_prompt_dict:
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# check if the base huggingface model has a registered custom prompt
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model_prompt_details = custom_prompt_dict[hf_model_name]
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prompt = custom_prompt(
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role_dict=model_prompt_details.get("roles", None),
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initial_prompt_value=model_prompt_details.get("initial_prompt_value", ""),
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final_prompt_value=model_prompt_details.get("final_prompt_value", ""),
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messages=messages,
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)
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else:
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if hf_model_name is None:
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if "llama-2" in model.lower(): # llama-2 model
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if "chat" in model.lower(): # apply llama2 chat template
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hf_model_name = "meta-llama/Llama-2-7b-chat-hf"
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else: # apply regular llama2 template
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hf_model_name = "meta-llama/Llama-2-7b"
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hf_model_name = (
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hf_model_name or model
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) # pass in hf model name for pulling it's prompt template - (e.g. `hf_model_name="meta-llama/Llama-2-7b-chat-hf` applies the llama2 chat template to the prompt)
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prompt = prompt_factory(model=hf_model_name, messages=messages)
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stream = inference_params.pop("stream", None)
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if stream == True:
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data = json.dumps(
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{"inputs": prompt, "parameters": inference_params, "stream": True}
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).encode("utf-8")
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if acompletion == True:
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response = async_streaming(
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optional_params=optional_params,
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encoding=encoding,
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model_response=model_response,
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model=model,
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logging_obj=logging_obj,
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data=data,
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model_id=model_id,
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aws_secret_access_key=aws_secret_access_key,
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aws_access_key_id=aws_access_key_id,
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aws_region_name=aws_region_name,
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)
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return response
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if model_id is not None:
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response = client.invoke_endpoint_with_response_stream(
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EndpointName=model,
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InferenceComponentName=model_id,
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ContentType="application/json",
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Body=data,
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CustomAttributes="accept_eula=true",
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)
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else:
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response = client.invoke_endpoint_with_response_stream(
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EndpointName=model,
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ContentType="application/json",
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Body=data,
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CustomAttributes="accept_eula=true",
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)
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return response["Body"]
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elif acompletion == True:
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_data = {"inputs": prompt, "parameters": inference_params}
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return async_completion(
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optional_params=optional_params,
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encoding=encoding,
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model_response=model_response,
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model=model,
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logging_obj=logging_obj,
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data=_data,
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model_id=model_id,
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aws_secret_access_key=aws_secret_access_key,
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aws_access_key_id=aws_access_key_id,
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aws_region_name=aws_region_name,
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)
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data = json.dumps({"inputs": prompt, "parameters": inference_params}).encode(
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"utf-8"
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)
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## COMPLETION CALL
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try:
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if model_id is not None:
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## LOGGING
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request_str = f"""
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response = client.invoke_endpoint(
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EndpointName={model},
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InferenceComponentName={model_id},
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ContentType="application/json",
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Body={data}, # type: ignore
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CustomAttributes="accept_eula=true",
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)
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""" # type: ignore
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logging_obj.pre_call(
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input=prompt,
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api_key="",
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additional_args={
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"complete_input_dict": data,
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"request_str": request_str,
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"hf_model_name": hf_model_name,
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},
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)
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response = client.invoke_endpoint(
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EndpointName=model,
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InferenceComponentName=model_id,
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ContentType="application/json",
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Body=data,
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CustomAttributes="accept_eula=true",
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)
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else:
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## LOGGING
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request_str = f"""
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response = client.invoke_endpoint(
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EndpointName={model},
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ContentType="application/json",
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Body={data}, # type: ignore
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CustomAttributes="accept_eula=true",
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)
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""" # type: ignore
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logging_obj.pre_call(
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input=prompt,
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api_key="",
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additional_args={
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"complete_input_dict": data,
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"request_str": request_str,
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"hf_model_name": hf_model_name,
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},
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)
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response = client.invoke_endpoint(
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EndpointName=model,
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ContentType="application/json",
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Body=data,
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CustomAttributes="accept_eula=true",
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)
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except Exception as e:
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status_code = (
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getattr(e, "response", {})
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.get("ResponseMetadata", {})
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.get("HTTPStatusCode", 500)
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)
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error_message = (
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getattr(e, "response", {}).get("Error", {}).get("Message", str(e))
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)
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if "Inference Component Name header is required" in error_message:
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error_message += "\n pass in via `litellm.completion(..., model_id={InferenceComponentName})`"
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raise SagemakerError(status_code=status_code, message=error_message)
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response = response["Body"].read().decode("utf8")
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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="",
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original_response=response,
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additional_args={"complete_input_dict": data},
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)
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print_verbose(f"raw model_response: {response}")
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## RESPONSE OBJECT
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completion_response = json.loads(response)
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try:
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if isinstance(completion_response, list):
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completion_response_choices = completion_response[0]
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else:
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completion_response_choices = completion_response
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completion_output = ""
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if "generation" in completion_response_choices:
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completion_output += completion_response_choices["generation"]
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elif "generated_text" in completion_response_choices:
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completion_output += completion_response_choices["generated_text"]
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# check if the prompt template is part of output, if so - filter it out
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if completion_output.startswith(prompt) and "<s>" in prompt:
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completion_output = completion_output.replace(prompt, "", 1)
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model_response["choices"][0]["message"]["content"] = completion_output
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except:
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raise SagemakerError(
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message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}",
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status_code=500,
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)
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## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
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prompt_tokens = len(encoding.encode(prompt))
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completion_tokens = len(
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encoding.encode(model_response["choices"][0]["message"].get("content", ""))
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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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return model_response
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async def async_streaming(
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optional_params,
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encoding,
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model_response: ModelResponse,
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model: str,
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model_id: Optional[str],
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logging_obj: Any,
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data,
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aws_secret_access_key: Optional[str],
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aws_access_key_id: Optional[str],
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aws_region_name: Optional[str],
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):
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"""
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Use aioboto3
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"""
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import aioboto3
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session = aioboto3.Session()
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if aws_access_key_id != None:
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# uses auth params passed to completion
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# aws_access_key_id is not None, assume user is trying to auth using litellm.completion
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_client = session.client(
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service_name="sagemaker-runtime",
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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region_name=aws_region_name,
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)
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else:
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# aws_access_key_id is None, assume user is trying to auth using env variables
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# boto3 automaticaly reads env variables
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|
|
|
# we need to read region name from env
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|
# I assume majority of users use .env for auth
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|
region_name = (
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get_secret("AWS_REGION_NAME")
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or "us-west-2" # default to us-west-2 if user not specified
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)
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_client = session.client(
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service_name="sagemaker-runtime",
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region_name=region_name,
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)
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async with _client as client:
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try:
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if model_id is not None:
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response = await client.invoke_endpoint_with_response_stream(
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EndpointName=model,
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InferenceComponentName=model_id,
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ContentType="application/json",
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Body=data,
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CustomAttributes="accept_eula=true",
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)
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else:
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response = await client.invoke_endpoint_with_response_stream(
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EndpointName=model,
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ContentType="application/json",
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Body=data,
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CustomAttributes="accept_eula=true",
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)
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except Exception as e:
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raise SagemakerError(status_code=500, message=f"{str(e)}")
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response = response["Body"]
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async for chunk in response:
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yield chunk
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|
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async def async_completion(
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optional_params,
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encoding,
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model_response: ModelResponse,
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model: str,
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logging_obj: Any,
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data: dict,
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model_id: Optional[str],
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aws_secret_access_key: Optional[str],
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aws_access_key_id: Optional[str],
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aws_region_name: Optional[str],
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):
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"""
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Use aioboto3
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"""
|
|
import aioboto3
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|
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session = aioboto3.Session()
|
|
|
|
if aws_access_key_id != None:
|
|
# uses auth params passed to completion
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# aws_access_key_id is not None, assume user is trying to auth using litellm.completion
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|
_client = session.client(
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service_name="sagemaker-runtime",
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aws_access_key_id=aws_access_key_id,
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aws_secret_access_key=aws_secret_access_key,
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region_name=aws_region_name,
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)
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|
else:
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# aws_access_key_id is None, assume user is trying to auth using env variables
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# boto3 automaticaly reads env variables
|
|
|
|
# we need to read region name from env
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|
# I assume majority of users use .env for auth
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|
region_name = (
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get_secret("AWS_REGION_NAME")
|
|
or "us-west-2" # default to us-west-2 if user not specified
|
|
)
|
|
_client = session.client(
|
|
service_name="sagemaker-runtime",
|
|
region_name=region_name,
|
|
)
|
|
|
|
async with _client as client:
|
|
encoded_data = json.dumps(data).encode("utf-8")
|
|
try:
|
|
if model_id is not None:
|
|
## LOGGING
|
|
request_str = f"""
|
|
response = client.invoke_endpoint(
|
|
EndpointName={model},
|
|
InferenceComponentName={model_id},
|
|
ContentType="application/json",
|
|
Body={data},
|
|
CustomAttributes="accept_eula=true",
|
|
)
|
|
""" # type: ignore
|
|
logging_obj.pre_call(
|
|
input=data["inputs"],
|
|
api_key="",
|
|
additional_args={
|
|
"complete_input_dict": data,
|
|
"request_str": request_str,
|
|
},
|
|
)
|
|
response = await client.invoke_endpoint(
|
|
EndpointName=model,
|
|
InferenceComponentName=model_id,
|
|
ContentType="application/json",
|
|
Body=encoded_data,
|
|
CustomAttributes="accept_eula=true",
|
|
)
|
|
else:
|
|
## LOGGING
|
|
request_str = f"""
|
|
response = client.invoke_endpoint(
|
|
EndpointName={model},
|
|
ContentType="application/json",
|
|
Body={data},
|
|
CustomAttributes="accept_eula=true",
|
|
)
|
|
""" # type: ignore
|
|
logging_obj.pre_call(
|
|
input=data["inputs"],
|
|
api_key="",
|
|
additional_args={
|
|
"complete_input_dict": data,
|
|
"request_str": request_str,
|
|
},
|
|
)
|
|
response = await client.invoke_endpoint(
|
|
EndpointName=model,
|
|
ContentType="application/json",
|
|
Body=encoded_data,
|
|
CustomAttributes="accept_eula=true",
|
|
)
|
|
except Exception as e:
|
|
error_message = f"{str(e)}"
|
|
if "Inference Component Name header is required" in error_message:
|
|
error_message += "\n pass in via `litellm.completion(..., model_id={InferenceComponentName})`"
|
|
raise SagemakerError(status_code=500, message=error_message)
|
|
response = await response["Body"].read()
|
|
response = response.decode("utf8")
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=data["inputs"],
|
|
api_key="",
|
|
original_response=response,
|
|
additional_args={"complete_input_dict": data},
|
|
)
|
|
## RESPONSE OBJECT
|
|
completion_response = json.loads(response)
|
|
try:
|
|
if isinstance(completion_response, list):
|
|
completion_response_choices = completion_response[0]
|
|
else:
|
|
completion_response_choices = completion_response
|
|
completion_output = ""
|
|
if "generation" in completion_response_choices:
|
|
completion_output += completion_response_choices["generation"]
|
|
elif "generated_text" in completion_response_choices:
|
|
completion_output += completion_response_choices["generated_text"]
|
|
|
|
# check if the prompt template is part of output, if so - filter it out
|
|
if completion_output.startswith(data["inputs"]) and "<s>" in data["inputs"]:
|
|
completion_output = completion_output.replace(data["inputs"], "", 1)
|
|
|
|
model_response["choices"][0]["message"]["content"] = completion_output
|
|
except:
|
|
raise SagemakerError(
|
|
message=f"LiteLLM Error: Unable to parse sagemaker RAW RESPONSE {json.dumps(completion_response)}",
|
|
status_code=500,
|
|
)
|
|
|
|
## CALCULATING USAGE - baseten charges on time, not tokens - have some mapping of cost here.
|
|
prompt_tokens = len(encoding.encode(data["inputs"]))
|
|
completion_tokens = len(
|
|
encoding.encode(model_response["choices"][0]["message"].get("content", ""))
|
|
)
|
|
|
|
model_response["created"] = int(time.time())
|
|
model_response["model"] = model
|
|
usage = Usage(
|
|
prompt_tokens=prompt_tokens,
|
|
completion_tokens=completion_tokens,
|
|
total_tokens=prompt_tokens + completion_tokens,
|
|
)
|
|
setattr(model_response, "usage", usage)
|
|
return model_response
|
|
|
|
|
|
def embedding(
|
|
model: str,
|
|
input: list,
|
|
model_response: EmbeddingResponse,
|
|
print_verbose: Callable,
|
|
encoding,
|
|
logging_obj,
|
|
custom_prompt_dict={},
|
|
optional_params=None,
|
|
litellm_params=None,
|
|
logger_fn=None,
|
|
):
|
|
"""
|
|
Supports Huggingface Jumpstart embeddings like GPT-6B
|
|
"""
|
|
### BOTO3 INIT
|
|
import boto3
|
|
|
|
# pop aws_secret_access_key, aws_access_key_id, aws_region_name from kwargs, since completion calls fail with them
|
|
aws_secret_access_key = optional_params.pop("aws_secret_access_key", None)
|
|
aws_access_key_id = optional_params.pop("aws_access_key_id", None)
|
|
aws_region_name = optional_params.pop("aws_region_name", None)
|
|
|
|
if aws_access_key_id is not None:
|
|
# uses auth params passed to completion
|
|
# aws_access_key_id is not None, assume user is trying to auth using litellm.completion
|
|
client = boto3.client(
|
|
service_name="sagemaker-runtime",
|
|
aws_access_key_id=aws_access_key_id,
|
|
aws_secret_access_key=aws_secret_access_key,
|
|
region_name=aws_region_name,
|
|
)
|
|
else:
|
|
# aws_access_key_id is None, assume user is trying to auth using env variables
|
|
# boto3 automaticaly reads env variables
|
|
|
|
# we need to read region name from env
|
|
# I assume majority of users use .env for auth
|
|
region_name = (
|
|
get_secret("AWS_REGION_NAME")
|
|
or "us-west-2" # default to us-west-2 if user not specified
|
|
)
|
|
client = boto3.client(
|
|
service_name="sagemaker-runtime",
|
|
region_name=region_name,
|
|
)
|
|
|
|
# pop streaming if it's in the optional params as 'stream' raises an error with sagemaker
|
|
inference_params = deepcopy(optional_params)
|
|
inference_params.pop("stream", None)
|
|
|
|
## Load Config
|
|
config = litellm.SagemakerConfig.get_config()
|
|
for k, v in config.items():
|
|
if (
|
|
k not in inference_params
|
|
): # completion(top_k=3) > sagemaker_config(top_k=3) <- allows for dynamic variables to be passed in
|
|
inference_params[k] = v
|
|
|
|
#### HF EMBEDDING LOGIC
|
|
data = json.dumps({"text_inputs": input}).encode("utf-8")
|
|
|
|
## LOGGING
|
|
request_str = f"""
|
|
response = client.invoke_endpoint(
|
|
EndpointName={model},
|
|
ContentType="application/json",
|
|
Body={data}, # type: ignore
|
|
CustomAttributes="accept_eula=true",
|
|
)""" # type: ignore
|
|
logging_obj.pre_call(
|
|
input=input,
|
|
api_key="",
|
|
additional_args={"complete_input_dict": data, "request_str": request_str},
|
|
)
|
|
## EMBEDDING CALL
|
|
try:
|
|
response = client.invoke_endpoint(
|
|
EndpointName=model,
|
|
ContentType="application/json",
|
|
Body=data,
|
|
CustomAttributes="accept_eula=true",
|
|
)
|
|
except Exception as e:
|
|
status_code = (
|
|
getattr(e, "response", {})
|
|
.get("ResponseMetadata", {})
|
|
.get("HTTPStatusCode", 500)
|
|
)
|
|
error_message = (
|
|
getattr(e, "response", {}).get("Error", {}).get("Message", str(e))
|
|
)
|
|
raise SagemakerError(status_code=status_code, message=error_message)
|
|
|
|
response = json.loads(response["Body"].read().decode("utf8"))
|
|
## LOGGING
|
|
logging_obj.post_call(
|
|
input=input,
|
|
api_key="",
|
|
original_response=response,
|
|
additional_args={"complete_input_dict": data},
|
|
)
|
|
|
|
print_verbose(f"raw model_response: {response}")
|
|
if "embedding" not in response:
|
|
raise SagemakerError(status_code=500, message="embedding not found in response")
|
|
embeddings = response["embedding"]
|
|
|
|
if not isinstance(embeddings, list):
|
|
raise SagemakerError(
|
|
status_code=422, message=f"Response not in expected format - {embeddings}"
|
|
)
|
|
|
|
output_data = []
|
|
for idx, embedding in enumerate(embeddings):
|
|
output_data.append(
|
|
{"object": "embedding", "index": idx, "embedding": embedding}
|
|
)
|
|
|
|
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))
|
|
|
|
model_response["usage"] = Usage(
|
|
prompt_tokens=input_tokens, completion_tokens=0, total_tokens=input_tokens
|
|
)
|
|
|
|
return model_response
|