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init nn4k module
(cherry picked from commit ec6c8e6f5e561aae04f49ef8340db203cb8e50e8)
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995dfab7f6
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python/NN4K/__init__.py
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python/NN4K/__init__.py
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python/NN4K/executor/__init__.py
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python/NN4K/executor/__init__.py
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python/NN4K/executor/base.py
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python/NN4K/executor/base.py
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from abc import ABC, abstractmethod
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class ModelExecutor(ABC):
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"""
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对应xflow AntLLM
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"""
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@classmethod
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def from_config(cls,
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args='sys',
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**kwargs):
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pass
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def __init__(self,
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backend_model,
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backend_tokenizer,
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init_args,
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**kwargs):
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self.backend_model = backend_model
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self.backend_tokenizer = backend_tokenizer
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self.init_args = init_args
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self.kwargs = kwargs
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class LLMExecutor(ModelExecutor):
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@abstractmethod
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def sft_train(self, args=None, callbacks=None, **kwargs):
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raise NotImplementedError("")
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@abstractmethod
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def rl_tuning(self, args=None, callbacks=None, **kwargs):
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raise NotImplementedError("")
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@abstractmethod
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def batch_inference(self, args, **kwargs):
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pass
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@abstractmethod
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def inference(self, input, inference_args, **kwargs):
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raise NotImplementedError()
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class HfLLMExecutor(ModelExecutor):
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pass
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class DeepKEExecutor(ModelExecutor):
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pass
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python/NN4K/executor/deepke/__init__.py
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python/NN4K/executor/deepke/__init__.py
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python/NN4K/executor/hugging_face/__init__.py
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python/NN4K/executor/hugging_face/__init__.py
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python/NN4K/invoker/__init__.py
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python/NN4K/invoker/__init__.py
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python/NN4K/invoker/base.py
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python/NN4K/invoker/base.py
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# coding: utf-8
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# Copyright (c) Antfin, Inc. All rights reserved.
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import sys
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from abc import ABC
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class ModelInvoker(ABC):
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"""
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对应 xflow ModelHubEntry
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"""
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def submit_sft(self, submit_mode='k8s'):
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pass
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def submit_rl_tuning(self, submit_mode='k8s'):
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pass
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def deploy(cls, args, deploy_mode='k8s'):
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pass
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def inference(self, input, **kwargs):
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"""
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这个是从已有的服务中获取inference
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Args:
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args:
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**kwargs:
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Returns:
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"""
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pass
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@classmethod
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def from_config(cls, args='sys'):
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return cls()
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class OpenAI(ModelInvoker):
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def __init__(self, token):
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self.token = token
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pass
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def inference(self, input, **kwargs):
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import requests
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requests.post(url="https://api.openai.com", params={"input": input, "token": self.token})
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