95 lines
3.4 KiB
Python
95 lines
3.4 KiB
Python
import logging
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import json
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import copy
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from federatedscope.core.message import Message
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from federatedscope.core.workers import Client
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logger = logging.getLogger(__name__)
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class FedExClient(Client):
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"""Some code snippets are borrowed from the open-sourced FedEx (
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https://github.com/mkhodak/FedEx)
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"""
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def _apply_hyperparams(self, hyperparams):
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"""Apply the given hyperparameters
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Arguments:
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hyperparams (dict): keys are hyperparameter names \
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and values are specific choices.
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"""
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cmd_args = []
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for k, v in hyperparams.items():
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cmd_args.append(k)
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cmd_args.append(v)
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self._cfg.defrost()
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self._cfg.merge_from_list(cmd_args, check_cfg=False)
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self._cfg.freeze(inform=False, check_cfg=False)
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self.trainer.cfg = self._cfg
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def callback_funcs_for_model_para(self, message: Message):
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round, sender, content = message.state, message.sender, message.content
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model_params, arms, hyperparams = content["model_param"], content[
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"arms"], content["hyperparam"]
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attempt = {
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'Role': 'Client #{:d}'.format(self.ID),
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'Round': self.state + 1,
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'Arms': arms,
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'Hyperparams': hyperparams
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}
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logger.info(json.dumps(attempt))
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self._apply_hyperparams(hyperparams)
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self.trainer.update(model_params)
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# self.model.load_state_dict(content)
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self.state = round
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sample_size, model_para_all, results = self.trainer.train()
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if self._cfg.federate.share_local_model and not \
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self._cfg.federate.online_aggr:
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model_para_all = copy.deepcopy(model_para_all)
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logger.info(
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self._monitor.format_eval_res(results,
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rnd=self.state,
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role='Client #{}'.format(self.ID),
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return_raw=True))
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results['arms'] = arms
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results['client_id'] = self.ID - 1
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content = (sample_size, model_para_all, results)
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self.comm_manager.send(
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Message(msg_type='model_para',
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sender=self.ID,
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receiver=[sender],
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state=self.state,
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content=content))
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def callback_funcs_for_evaluate(self, message: Message):
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sender = message.sender
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self.state = message.state
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if message.content is not None:
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model_params = message.content["model_param"]
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self.trainer.update(model_params)
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if self._cfg.finetune.before_eval:
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self.trainer.finetune()
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metrics = {}
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for split in self._cfg.eval.split:
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eval_metrics = self.trainer.evaluate(target_data_split_name=split)
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for key in eval_metrics:
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if self._cfg.federate.mode == 'distributed':
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logger.info('Client #{:d}: (Evaluation ({:s} set) at '
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'Round #{:d}) {:s} is {:.6f}'.format(
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self.ID, split, self.state, key,
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eval_metrics[key]))
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metrics.update(**eval_metrics)
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self.comm_manager.send(
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Message(msg_type='metrics',
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sender=self.ID,
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receiver=[sender],
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state=self.state,
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content=metrics))
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