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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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@ -8,6 +8,7 @@ import litellm
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from litellm.utils import ModelResponse, Usage
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from .prompt_templates.factory import prompt_factory, custom_prompt
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class PetalsError(Exception):
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def __init__(self, status_code, message):
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self.status_code = status_code
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@ -16,7 +17,8 @@ class PetalsError(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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class PetalsConfig():
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class PetalsConfig:
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"""
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Reference: https://github.com/petals-infra/chat.petals.dev#post-apiv1generate
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The `PetalsConfig` class encapsulates the configuration for the Petals API. The properties of this class are described below:
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@ -30,45 +32,64 @@ class PetalsConfig():
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- `do_sample` (boolean, optional): If set to 0 (default), the API runs greedy generation. If set to 1, the API performs sampling using the parameters below:
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- `temperature` (float, optional): This value sets the temperature for sampling.
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- `top_k` (integer, optional): This value sets the limit for top-k sampling.
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- `top_p` (float, optional): This value sets the limit for top-p (nucleus) sampling.
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- `repetition_penalty` (float, optional): This helps apply the repetition penalty during text generation, as discussed in this paper.
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"""
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max_length: Optional[int]=None
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max_new_tokens: Optional[int]=litellm.max_tokens # petals requires max tokens to be set
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do_sample: Optional[bool]=None
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temperature: Optional[float]=None
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top_k: Optional[int]=None
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top_p: Optional[float]=None
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repetition_penalty: Optional[float]=None
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def __init__(self,
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max_length: Optional[int]=None,
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max_new_tokens: Optional[int]=litellm.max_tokens, # petals requires max tokens to be set
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do_sample: Optional[bool]=None,
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temperature: Optional[float]=None,
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top_k: Optional[int]=None,
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top_p: Optional[float]=None,
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repetition_penalty: Optional[float]=None) -> None:
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max_length: Optional[int] = None
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max_new_tokens: Optional[
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int
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] = litellm.max_tokens # petals requires max tokens to be set
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do_sample: Optional[bool] = None
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temperature: Optional[float] = None
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top_k: Optional[int] = None
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top_p: Optional[float] = None
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repetition_penalty: Optional[float] = None
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def __init__(
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self,
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max_length: Optional[int] = None,
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max_new_tokens: Optional[
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int
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] = litellm.max_tokens, # petals requires max tokens to be set
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do_sample: Optional[bool] = None,
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temperature: Optional[float] = None,
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top_k: Optional[int] = None,
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top_p: Optional[float] = None,
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repetition_penalty: Optional[float] = 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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def completion(
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model: str,
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messages: list,
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api_base: Optional[str],
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api_base: Optional[str],
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model_response: ModelResponse,
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print_verbose: Callable,
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encoding,
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@ -80,96 +101,97 @@ def completion(
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):
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## Load Config
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config = litellm.PetalsConfig.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) > petals_config(top_k=3) <- allows for dynamic variables to be passed in
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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) > petals_config(top_k=3) <- allows for dynamic variables to be passed in
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optional_params[k] = v
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if model in litellm.custom_prompt_dict:
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# check if the model has a registered custom prompt
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model_prompt_details = litellm.custom_prompt_dict[model]
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prompt = custom_prompt(
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role_dict=model_prompt_details["roles"],
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initial_prompt_value=model_prompt_details["initial_prompt_value"],
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final_prompt_value=model_prompt_details["final_prompt_value"],
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messages=messages
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role_dict=model_prompt_details["roles"],
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initial_prompt_value=model_prompt_details["initial_prompt_value"],
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final_prompt_value=model_prompt_details["final_prompt_value"],
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messages=messages,
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)
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else:
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prompt = prompt_factory(model=model, messages=messages)
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if api_base:
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if api_base:
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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="",
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additional_args={"complete_input_dict": optional_params, "api_base": api_base},
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)
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data = {
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"model": model,
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"inputs": prompt,
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**optional_params
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}
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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": optional_params,
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"api_base": api_base,
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},
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)
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data = {"model": model, "inputs": prompt, **optional_params}
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## COMPLETION CALL
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response = requests.post(api_base, data=data)
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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.text,
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additional_args={"complete_input_dict": optional_params},
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)
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input=prompt,
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api_key="",
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original_response=response.text,
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additional_args={"complete_input_dict": optional_params},
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)
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## RESPONSE OBJECT
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try:
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output_text = response.json()["outputs"]
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except Exception as e:
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PetalsError(status_code=response.status_code, message=str(e))
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else:
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else:
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try:
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import torch
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from transformers import AutoTokenizer
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from petals import AutoDistributedModelForCausalLM # type: ignore
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from petals import AutoDistributedModelForCausalLM # type: ignore
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except:
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raise Exception(
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"Importing torch, transformers, petals failed\nTry pip installing petals \npip install git+https://github.com/bigscience-workshop/petals"
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)
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model = model
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tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False, add_bos_token=False)
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tokenizer = AutoTokenizer.from_pretrained(
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model, use_fast=False, add_bos_token=False
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)
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model_obj = AutoDistributedModelForCausalLM.from_pretrained(model)
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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="",
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additional_args={"complete_input_dict": optional_params},
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)
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input=prompt,
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api_key="",
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additional_args={"complete_input_dict": optional_params},
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)
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## COMPLETION CALL
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inputs = tokenizer(prompt, return_tensors="pt")["input_ids"]
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# optional params: max_new_tokens=1,temperature=0.9, top_p=0.6
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outputs = model_obj.generate(inputs, **optional_params)
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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=outputs,
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additional_args={"complete_input_dict": optional_params},
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)
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input=prompt,
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api_key="",
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original_response=outputs,
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additional_args={"complete_input_dict": optional_params},
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)
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## RESPONSE OBJECT
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output_text = tokenizer.decode(outputs[0])
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if len(output_text) > 0:
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model_response["choices"][0]["message"]["content"] = output_text
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prompt_tokens = len(
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encoding.encode(prompt)
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)
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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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@ -177,13 +199,14 @@ def completion(
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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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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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model_response.usage = usage
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return model_response
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def embedding():
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# logic for parsing in - calling - parsing out model embedding calls
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pass
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