forked from phoenix/litellm-mirror
refactor: add black formatting
This commit is contained in:
Krrish Dholakia
parent
b87d630b0a
commit
4905929de3
156 changed files with 19723 additions and 10869 deletions
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@ -4,81 +4,100 @@ import requests
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import time
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from typing import Callable, Optional
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from litellm.utils import ModelResponse, Usage
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import litellm
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import litellm
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import httpx
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from .prompt_templates.factory import prompt_factory, custom_prompt
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class ReplicateError(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(method="POST", url="https://api.replicate.com/v1/deployments")
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self.request = httpx.Request(
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method="POST", url="https://api.replicate.com/v1/deployments"
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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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class ReplicateConfig():
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class ReplicateConfig:
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"""
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Reference: https://replicate.com/meta/llama-2-70b-chat/api
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- `prompt` (string): The prompt to send to the model.
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- `system_prompt` (string): The system prompt to send to the model. This is prepended to the prompt and helps guide system behavior. Default value: `You are a helpful assistant`.
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- `max_new_tokens` (integer): Maximum number of tokens to generate. Typically, a word is made up of 2-3 tokens. Default value: `128`.
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- `min_new_tokens` (integer): Minimum number of tokens to generate. To disable, set to `-1`. A word is usually 2-3 tokens. Default value: `-1`.
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- `temperature` (number): Adjusts the randomness of outputs. Values greater than 1 increase randomness, 0 is deterministic, and 0.75 is a reasonable starting value. Default value: `0.75`.
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- `top_p` (number): During text decoding, it samples from the top `p` percentage of most likely tokens. Reduce this to ignore less probable tokens. Default value: `0.9`.
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- `top_k` (integer): During text decoding, samples from the top `k` most likely tokens. Reduce this to ignore less probable tokens. Default value: `50`.
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- `stop_sequences` (string): A comma-separated list of sequences to stop generation at. For example, inputting '<end>,<stop>' will cease generation at the first occurrence of either 'end' or '<stop>'.
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- `seed` (integer): This is the seed for the random generator. Leave it blank to randomize the seed.
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- `debug` (boolean): If set to `True`, it provides debugging output in logs.
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Please note that Replicate's mapping of these parameters can be inconsistent across different models, indicating that not all of these parameters may be available for use with all models.
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"""
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system_prompt: Optional[str]=None
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max_new_tokens: Optional[int]=None
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min_new_tokens: Optional[int]=None
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temperature: Optional[int]=None
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top_p: Optional[int]=None
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top_k: Optional[int]=None
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stop_sequences: Optional[str]=None
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seed: Optional[int]=None
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debug: Optional[bool]=None
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def __init__(self,
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system_prompt: Optional[str]=None,
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max_new_tokens: Optional[int]=None,
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min_new_tokens: Optional[int]=None,
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temperature: Optional[int]=None,
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top_p: Optional[int]=None,
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top_k: Optional[int]=None,
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stop_sequences: Optional[str]=None,
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seed: Optional[int]=None,
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debug: Optional[bool]=None) -> None:
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system_prompt: Optional[str] = None
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max_new_tokens: Optional[int] = None
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min_new_tokens: Optional[int] = None
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temperature: Optional[int] = None
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top_p: Optional[int] = None
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top_k: Optional[int] = None
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stop_sequences: Optional[str] = None
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seed: Optional[int] = None
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debug: Optional[bool] = None
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def __init__(
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self,
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system_prompt: Optional[str] = None,
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max_new_tokens: Optional[int] = None,
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min_new_tokens: Optional[int] = None,
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temperature: Optional[int] = None,
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top_p: Optional[int] = None,
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top_k: Optional[int] = None,
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stop_sequences: Optional[str] = None,
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seed: Optional[int] = None,
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debug: 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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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 {k: v for k, v in cls.__dict__.items()
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if not k.startswith('__')
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and not isinstance(v, (types.FunctionType, types.BuiltinFunctionType, classmethod, staticmethod))
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and v is not None}
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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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# Function to start a prediction and get the prediction URL
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def start_prediction(version_id, input_data, api_token, api_base, logging_obj, print_verbose):
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def start_prediction(
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version_id, input_data, api_token, api_base, logging_obj, print_verbose
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):
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base_url = api_base
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if "deployments" in version_id:
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print_verbose("\nLiteLLM: Request to custom replicate deployment")
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@ -88,7 +107,7 @@ def start_prediction(version_id, input_data, api_token, api_base, logging_obj, p
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headers = {
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"Authorization": f"Token {api_token}",
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"Content-Type": "application/json"
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"Content-Type": "application/json",
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}
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initial_prediction_data = {
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@ -98,24 +117,33 @@ def start_prediction(version_id, input_data, api_token, api_base, logging_obj, p
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## LOGGING
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logging_obj.pre_call(
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input=input_data["prompt"],
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api_key="",
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additional_args={"complete_input_dict": initial_prediction_data, "headers": headers, "api_base": base_url},
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input=input_data["prompt"],
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api_key="",
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additional_args={
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"complete_input_dict": initial_prediction_data,
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"headers": headers,
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"api_base": base_url,
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},
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)
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response = requests.post(f"{base_url}/predictions", json=initial_prediction_data, headers=headers)
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response = requests.post(
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f"{base_url}/predictions", json=initial_prediction_data, headers=headers
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)
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if response.status_code == 201:
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response_data = response.json()
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return response_data.get("urls", {}).get("get")
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else:
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raise ReplicateError(response.status_code, f"Failed to start prediction {response.text}")
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raise ReplicateError(
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response.status_code, f"Failed to start prediction {response.text}"
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)
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# Function to handle prediction response (non-streaming)
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def handle_prediction_response(prediction_url, api_token, print_verbose):
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output_string = ""
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headers = {
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"Authorization": f"Token {api_token}",
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"Content-Type": "application/json"
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"Content-Type": "application/json",
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}
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status = ""
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@ -127,18 +155,22 @@ def handle_prediction_response(prediction_url, api_token, print_verbose):
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if response.status_code == 200:
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response_data = response.json()
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if "output" in response_data:
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output_string = "".join(response_data['output'])
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output_string = "".join(response_data["output"])
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print_verbose(f"Non-streamed output:{output_string}")
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status = response_data.get('status', None)
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status = response_data.get("status", None)
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logs = response_data.get("logs", "")
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if status == "failed":
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replicate_error = response_data.get("error", "")
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raise ReplicateError(status_code=400, message=f"Error: {replicate_error}, \nReplicate logs:{logs}")
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raise ReplicateError(
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status_code=400,
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message=f"Error: {replicate_error}, \nReplicate logs:{logs}",
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)
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else:
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# this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed"
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print_verbose("Replicate: Failed to fetch prediction status and output.")
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return output_string, logs
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# Function to handle prediction response (streaming)
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def handle_prediction_response_streaming(prediction_url, api_token, print_verbose):
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previous_output = ""
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@ -146,30 +178,34 @@ def handle_prediction_response_streaming(prediction_url, api_token, print_verbos
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headers = {
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"Authorization": f"Token {api_token}",
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"Content-Type": "application/json"
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"Content-Type": "application/json",
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}
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status = ""
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while True and (status not in ["succeeded", "failed", "canceled"]):
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time.sleep(0.5) # prevent being rate limited by replicate
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time.sleep(0.5) # prevent being rate limited by replicate
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print_verbose(f"replicate: polling endpoint: {prediction_url}")
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response = requests.get(prediction_url, headers=headers)
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if response.status_code == 200:
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response_data = response.json()
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status = response_data['status']
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status = response_data["status"]
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if "output" in response_data:
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output_string = "".join(response_data['output'])
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new_output = output_string[len(previous_output):]
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output_string = "".join(response_data["output"])
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new_output = output_string[len(previous_output) :]
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print_verbose(f"New chunk: {new_output}")
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yield {"output": new_output, "status": status}
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previous_output = output_string
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status = response_data['status']
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status = response_data["status"]
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if status == "failed":
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replicate_error = response_data.get("error", "")
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raise ReplicateError(status_code=400, message=f"Error: {replicate_error}")
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raise ReplicateError(
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status_code=400, message=f"Error: {replicate_error}"
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)
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else:
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# this can fail temporarily but it does not mean the replicate request failed, replicate request fails when status=="failed"
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print_verbose(f"Replicate: Failed to fetch prediction status and output.{response.status_code}{response.text}")
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print_verbose(
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f"Replicate: Failed to fetch prediction status and output.{response.status_code}{response.text}"
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)
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# Function to extract version ID from model string
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def model_to_version_id(model):
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@ -178,11 +214,12 @@ def model_to_version_id(model):
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return split_model[1]
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return model
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# Main function for prediction completion
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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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api_base: str,
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model_response: ModelResponse,
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print_verbose: Callable,
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logging_obj,
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@ -196,35 +233,37 @@ def completion(
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# Start a prediction and get the prediction URL
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version_id = model_to_version_id(model)
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## Load Config
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config = litellm.ReplicateConfig.get_config()
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for k, v in config.items():
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if k not in optional_params: # completion(top_k=3) > replicate_config(top_k=3) <- allows for dynamic variables to be passed in
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config = litellm.ReplicateConfig.get_config()
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for k, v in config.items():
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if (
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k not in optional_params
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): # completion(top_k=3) > replicate_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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system_prompt = None
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if optional_params is not None and "supports_system_prompt" in optional_params:
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supports_sys_prompt = optional_params.pop("supports_system_prompt")
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else:
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supports_sys_prompt = False
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if supports_sys_prompt:
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for i in range(len(messages)):
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if messages[i]["role"] == "system":
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first_sys_message = messages.pop(i)
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system_prompt = first_sys_message["content"]
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break
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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", {}),
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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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bos_token=model_prompt_details.get("bos_token", ""),
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eos_token=model_prompt_details.get("eos_token", ""),
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messages=messages,
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)
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role_dict=model_prompt_details.get("roles", {}),
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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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bos_token=model_prompt_details.get("bos_token", ""),
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eos_token=model_prompt_details.get("eos_token", ""),
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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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@ -233,43 +272,58 @@ def completion(
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input_data = {
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"prompt": prompt,
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"system_prompt": system_prompt,
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**optional_params
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**optional_params,
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}
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# Otherwise, use the prompt as is
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else:
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input_data = {
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"prompt": prompt,
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**optional_params
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}
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input_data = {"prompt": prompt, **optional_params}
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## COMPLETION CALL
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## Replicate Compeltion calls have 2 steps
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## Step1: Start Prediction: gets a prediction url
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## Step2: Poll prediction url for response
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## Step2: is handled with and without streaming
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model_response["created"] = int(time.time()) # for pricing this must remain right before calling api
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prediction_url = start_prediction(version_id, input_data, api_key, api_base, logging_obj=logging_obj, print_verbose=print_verbose)
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model_response["created"] = int(
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time.time()
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) # for pricing this must remain right before calling api
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prediction_url = start_prediction(
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version_id,
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input_data,
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api_key,
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api_base,
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logging_obj=logging_obj,
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print_verbose=print_verbose,
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)
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print_verbose(prediction_url)
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# Handle the prediction response (streaming or non-streaming)
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if "stream" in optional_params and optional_params["stream"] == True:
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print_verbose("streaming request")
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return handle_prediction_response_streaming(prediction_url, api_key, print_verbose)
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return handle_prediction_response_streaming(
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prediction_url, api_key, print_verbose
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)
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else:
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result, logs = handle_prediction_response(prediction_url, api_key, print_verbose)
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model_response["ended"] = time.time() # for pricing this must remain right after calling api
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result, logs = handle_prediction_response(
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prediction_url, api_key, print_verbose
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)
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model_response[
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"ended"
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] = time.time() # for pricing this must remain right after calling api
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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=result,
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additional_args={"complete_input_dict": input_data,"logs": logs, "api_base": prediction_url, },
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input=prompt,
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api_key="",
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original_response=result,
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additional_args={
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"complete_input_dict": input_data,
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"logs": logs,
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"api_base": prediction_url,
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},
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)
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print_verbose(f"raw model_response: {result}")
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if len(result) == 0: # edge case, where result from replicate is empty
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if len(result) == 0: # edge case, where result from replicate is empty
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result = " "
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## Building RESPONSE OBJECT
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@ -278,12 +332,14 @@ def completion(
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# Calculate usage
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prompt_tokens = len(encoding.encode(prompt))
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completion_tokens = len(encoding.encode(model_response["choices"][0]["message"].get("content", "")))
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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["model"] = "replicate/" + 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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total_tokens=prompt_tokens + completion_tokens,
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)
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model_response.usage = usage
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return model_response
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