Source code for fl4health.mixins.adaptive_drift_constrained

"""AdaptiveDriftConstrainedMixin."""

import warnings
from logging import INFO, WARN
from typing import Any, Protocol, runtime_checkable

import torch
from flwr.common.logger import log
from flwr.common.typing import Config, NDArrays

from fl4health.clients.flexible.base import FlexibleClient
from fl4health.losses.weight_drift_loss import WeightDriftLoss
from fl4health.mixins.core_protocols import FlexibleClientProtocol, FlexibleClientProtocolPreSetup
from fl4health.parameter_exchange.full_exchanger import FullParameterExchanger
from fl4health.parameter_exchange.packing_exchanger import FullParameterExchangerWithPacking
from fl4health.parameter_exchange.parameter_exchanger_base import ParameterExchanger
from fl4health.parameter_exchange.parameter_packer import ParameterPackerAdaptiveConstraint
from fl4health.utils.losses import TrainingLosses
from fl4health.utils.typing import TorchInputType, TorchPredType, TorchTargetType


[docs] @runtime_checkable class AdaptiveDriftConstrainedProtocol(FlexibleClientProtocol, Protocol): loss_for_adaptation: float drift_penalty_tensors: list[torch.Tensor] | None drift_penalty_weight: float | None penalty_loss_function: WeightDriftLoss parameter_exchanger: FullParameterExchangerWithPacking[float]
[docs] def compute_penalty_loss(self) -> torch.Tensor: ...
[docs] class AdaptiveDriftConstrainedMixin:
[docs] def __init__(self, *args: Any, **kwargs: Any): """ Adaptive Drift Constrained Mixin. To be used with `~fl4health.BaseClient` in order to add the ability to compute losses via a constrained adaptive drift. NOTE: Rather than using `AdaptiveDriftConstraintClient`, if a client subclasses `FlexibleClient`, than this mixin could be used on that subclass to implement the adaptive drift constraint. Raises: RuntimeError: when the inheriting class does not satisfy `FlexibleClientProtocolPreSetup`. """ # Initialize mixin-specific attributes with default values self.loss_for_adaptation = 0.1 self.drift_penalty_tensors = None self.drift_penalty_weight = None # Call parent's init try: super().__init__(*args, **kwargs) except TypeError: # if a parent class doesn't take args/kwargs super().__init__() # set penalty_loss_function if not isinstance(self, FlexibleClientProtocolPreSetup): raise RuntimeError("This object needs to satisfy `FlexibleClientProtocolPreSetup`.") self.penalty_loss_function = WeightDriftLoss(self.device)
def __init_subclass__(cls, **kwargs: Any): """This method is called when a class inherits from AdaptiveDriftConstrainedMixin.""" super().__init_subclass__(**kwargs) # Skip check for other mixins if cls.__name__.endswith("Mixin"): return # Skip validation for dynamically created classes if hasattr(cls, "_dynamically_created"): return # Check at class definition time if the parent class satisfies FlexibleClientProtocol for base in cls.__bases__: if base is not AdaptiveDriftConstrainedMixin and issubclass(base, FlexibleClient): return # If we get here, no compatible base was found msg = ( f"Class {cls.__name__} inherits from AdaptiveDriftConstrainedMixin but none of its other " f"base classes is a FlexibleClient. This may cause runtime errors." ) log(WARN, msg) warnings.warn(msg, RuntimeWarning, stacklevel=2)
[docs] def get_parameters(self: AdaptiveDriftConstrainedProtocol, config: Config) -> NDArrays: """ Packs the parameters and training loss into a single ``NDArrays`` to be sent to the server for aggregation. If the client has not been initialized, this means the server is requesting parameters for initialization and just the model parameters are sent. When using the ``FedAvgWithAdaptiveConstraint`` strategy, this should not happen, as that strategy requires server-side initialization parameters. However, other strategies may handle this case. Args: config (Config): Configurations to allow for customization of this functions behavior Returns: NDArrays: Parameters and training loss packed together into a list of numpy arrays to be sent to the server """ if not self.initialized: log(INFO, "Setting up client and providing full model parameters to the server for initialization") # If initialized is False, the server is requesting model parameters from which to initialize all other # clients. As such get_parameters is being called before fit or evaluate, so we must call # setup_client first. self.setup_client(config) # Need all parameters even if normally exchanging partial return FullParameterExchanger().push_parameters(self.model, config=config) # Make sure the proper components are there assert self.model is not None and self.parameter_exchanger is not None and self.loss_for_adaptation is not None model_weights = self.parameter_exchanger.push_parameters(self.model, config=config) # Weights and training loss sent to server for aggregation. Training loss is sent because server will # decide to increase or decrease the penalty weight, if adaptivity is turned on. return self.parameter_exchanger.pack_parameters(model_weights, self.loss_for_adaptation)
[docs] def set_parameters( self: AdaptiveDriftConstrainedProtocol, parameters: NDArrays, config: Config, fitting_round: bool ) -> None: """ Assumes that the parameters being passed contain model parameters concatenated with a penalty weight. They are unpacked for the clients to use in training. In the first fitting round, we assume the full model is being initialized and use the ``FullParameterExchanger()`` to set all model weights. Args: parameters (NDArrays): Parameters have information about model state to be added to the relevant client model and also the penalty weight to be applied during training. config (Config): The config is sent by the FL server to allow for customization in the function if desired. fitting_round (bool): Boolean that indicates whether the current federated learning round is a fitting round or an evaluation round. This is used to help determine which parameter exchange should be used for pulling parameters. A full parameter exchanger is always used if the current federated learning round is the very first fitting round. """ assert self.model is not None and self.parameter_exchanger is not None server_model_state, self.drift_penalty_weight = self.parameter_exchanger.unpack_parameters(parameters) log(INFO, f"Penalty weight received from the server: {self.drift_penalty_weight}") super().set_parameters(server_model_state, config, fitting_round) # type: ignore[safe-super]
[docs] def train_step( self: AdaptiveDriftConstrainedProtocol, input: TorchInputType, target: TorchTargetType ) -> tuple[TrainingLosses, TorchPredType]: losses, preds = self._compute_preds_and_losses(self.model, self.optimizers["global"], input, target) loss_clone = losses.backward["backward"].clone() # apply penalty penalty_loss = self.compute_penalty_loss() losses.backward["backward"] = losses.backward["backward"] + penalty_loss losses = self._apply_backwards_on_losses_and_take_step(self.model, self.optimizers["global"], losses) # prepare return values additional_losses = { "penalty_loss": penalty_loss.clone(), "local_loss": loss_clone, "loss_for_adaptation": loss_clone.clone(), } losses.additional_losses = additional_losses return losses, preds
[docs] def get_parameter_exchanger(self: AdaptiveDriftConstrainedProtocol, config: Config) -> ParameterExchanger: """ Setting up the parameter exchanger to include the appropriate packing functionality. By default we assume that we're exchanging all parameters. Can be overridden for other behavior. Args: config (Config): The config is sent by the FL server to allow for customization in the function if desired. Returns: ParameterExchanger: Exchanger that can handle packing/unpacking auxiliary server information. """ return FullParameterExchangerWithPacking(ParameterPackerAdaptiveConstraint())
[docs] def update_after_train( self: AdaptiveDriftConstrainedProtocol, local_steps: int, loss_dict: dict[str, float], config: Config ) -> None: """ Called after training with the number of ``local_steps`` performed over the FL round and the corresponding loss dictionary. We use this to store the training loss that we want to use to adapt the penalty weight parameter on the server side. Args: local_steps (int): The number of steps so far in the round in the local training. loss_dict (dict[str, float]): A dictionary of losses from local training. config (Config): The config from the server """ assert "loss_for_adaptation" in loss_dict # Store current loss which is the vanilla loss without the penalty term added in self.loss_for_adaptation = loss_dict["loss_for_adaptation"] super().update_after_train(local_steps, loss_dict, config) # type: ignore[safe-super]
[docs] def compute_penalty_loss(self: AdaptiveDriftConstrainedProtocol) -> torch.Tensor: """ Computes the drift loss for the client model and drift tensors. Returns: torch.Tensor: Computed penalty loss tensor """ # Penalty tensors must have been set for these clients. assert self.drift_penalty_tensors is not None return self.penalty_loss_function(self.model, self.drift_penalty_tensors, self.drift_penalty_weight)
[docs] def apply_adaptive_drift_to_client(client_base_type: type[FlexibleClient]) -> type[FlexibleClient]: """ Dynamically create an adapted client class. Args: client_base_type (type[FlexibleClient]): The class to be mixed. Returns: type[FlexibleClient]: A basic client that has been mixed with `AdaptiveDriftConstrainedMixin`. """ return type( f"AdaptiveDrift{client_base_type.__name__}", ( AdaptiveDriftConstrainedMixin, client_base_type, ), { # Special flag to bypass validation "_dynamically_created": True }, )