[Feature] Add BasicClientProtocol and AdaptiveDriftConstrainedMixin

fl4health/mixins/__init__.py

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+from .adaptive_drift_constrained import AdaptiveDriftConstrainedMixin
+
+__all__ = ["AdaptiveDriftConstrainedMixin"]

fl4health/mixins/adaptive_drift_constrained.py

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+"""AdaptiveDriftConstrainedMixin"""
+
+import warnings
+from logging import INFO
+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.basic_client import BasicClient
+from fl4health.losses.weight_drift_loss import WeightDriftLoss
+from fl4health.mixins.core import BasicClientProtocol, BasicClientProtocolPreSetup
+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 TorchFeatureType, TorchPredType, TorchTargetType
+
+
+@runtime_checkable
+class AdaptiveDriftConstrainedProtocol(BasicClientProtocol, 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]
+
+    def compute_penalty_loss(self) -> torch.Tensor: ...  # noqa: E704
+
+    def ensure_protocol_compliance(self) -> None: ...  # noqa: E704
+
+
+class AdaptiveDriftConstrainedMixin:
+    """Adaptive Drift Constrained Mixin
+
+    To be used with `~fl4health.BaseClient` in order to add the ability to compute
+    losses via a constrained adaptive drift.
+    """
+
+    def __init__(self, *args: Any, **kwargs: Any):
+        # 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:
+            super().__init__()
+
+        # set penalty_loss_function
+        if not isinstance(self, BasicClientProtocolPreSetup):
+            raise RuntimeError("This object needs to satisfy `BasicClientProtocolPreSetup`.")
+        self.penalty_loss_function = WeightDriftLoss(self.device)
+
+    def __init_subclass__(cls, **kwargs: Any):
+        """This method is called when a class inherits from AdaptiveMixin"""
+        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 BasicClientProtocol
+        for base in cls.__bases__:
+            if base is not AdaptiveDriftConstrainedMixin and isinstance(base, BasicClient):
+                return
+
+        # If we get here, no compatible base was found
+        warnings.warn(
+            f"Class {cls.__name__} inherits from AdaptiveMixin but none of its other "
+            f"base classes is a BasicClient. This may cause runtime errors.",
+            RuntimeWarning,
+        )
+
+    def ensure_protocol_compliance(self) -> None:
+        """Call this after the object is fully initialized"""
+        if not isinstance(self, BasicClient):
+            raise TypeError("Protocol requirements not met.")
+
+    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)
+        else:
+
+            # 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.
+            packed_params = self.parameter_exchanger.pack_parameters(model_weights, self.loss_for_adaptation)
+            return packed_params
+
+    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]
+
+    def compute_training_loss(
+        self: AdaptiveDriftConstrainedProtocol,
+        preds: TorchPredType,
+        features: TorchFeatureType,
+        target: TorchTargetType,
+    ) -> TrainingLosses:
+        """
+        Computes training loss given predictions of the model and ground truth data. Adds to objective by including
+        penalty loss.
+
+        Args:
+            preds (TorchPredType): Prediction(s) of the model(s) indexed by name. All predictions included in
+                dictionary will be used to compute metrics.
+            features: (TorchFeatureType): Feature(s) of the model(s) indexed by name.
+            target: (TorchTargetType): Ground truth data to evaluate predictions against.
+
+        Returns:
+            TrainingLosses: An instance of ``TrainingLosses`` containing backward loss and additional losses indexed
+            by name. Additional losses includes penalty loss.
+        """
+        loss, additional_losses = self.compute_loss_and_additional_losses(preds, features, target)
+        if additional_losses is None:
+            additional_losses = {}
+
+        additional_losses["loss"] = loss.clone()
+        # adding the vanilla loss to the additional losses to be used by update_after_train for potential adaptation
+        additional_losses["loss_for_adaptation"] = loss.clone()
+
+        # Compute the drift penalty loss and store it in the additional losses dictionary.
+        penalty_loss = self.compute_penalty_loss()
+        additional_losses["penalty_loss"] = penalty_loss.clone()
+
+        return TrainingLosses(backward=loss + penalty_loss, additional_losses=additional_losses)
+
+    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())
+
+    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]
+
+    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)

fl4health/mixins/core.py

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+from typing import Protocol, runtime_checkable
+
+import torch
+import torch.nn as nn
+from flwr.common.typing import Config, NDArrays, Scalar
+from torch.nn.modules.loss import _Loss
+from torch.optim import Optimizer
+from torch.utils.data import DataLoader
+
+from fl4health.utils.typing import TorchFeatureType, TorchPredType, TorchTargetType
+
+
+@runtime_checkable
+class NumPyClientMinimalProtocol(Protocol):
+    """A minimal protocol for NumPyClient with just essential methods."""
+
+    def get_parameters(self, config: dict[str, Scalar]) -> NDArrays:
+        pass
+
+    def fit(self, parameters: NDArrays, config: dict[str, Scalar]) -> tuple[NDArrays, int, dict[str, Scalar]]:
+        pass
+
+    def evaluate(self, parameters: NDArrays, config: dict[str, Scalar]) -> tuple[float, int, dict[str, Scalar]]:
+        pass
+
+    def set_parameters(self, parameters: NDArrays, config: Config, fitting_round: bool) -> None:
+        pass
+
+    def update_after_train(self, local_steps: int, loss_dict: dict[str, float], config: Config) -> None:
+        pass
+
+
+@runtime_checkable
+class BasicClientProtocolPreSetup(NumPyClientMinimalProtocol, Protocol):
+    """A minimal protocol for BasicClient focused on methods."""
+
+    device: torch.device
+    initialized: bool
+
+    # Include only methods, not attributes that get initialized later
+    def setup_client(self, config: Config) -> None:
+        pass
+
+    def get_model(self, config: Config) -> nn.Module:
+        pass
+
+    def get_data_loaders(self, config: Config) -> tuple[DataLoader, ...]:
+        pass
+
+    def get_optimizer(self, config: Config) -> Optimizer | dict[str, Optimizer]:
+        pass
+
+    def get_criterion(self, config: Config) -> _Loss:
+        pass
+
+    def compute_loss_and_additional_losses(
+        self, preds: TorchPredType, features: TorchFeatureType, target: TorchTargetType
+    ) -> tuple[torch.Tensor, dict[str, torch.Tensor] | None]:
+        pass
+
+
+@runtime_checkable
+class BasicClientProtocol(BasicClientProtocolPreSetup, Protocol):
+    """A minimal protocol for BasicClient focused on methods."""
+
+    model: nn.Module

tests/mixins/test_adaptive_drift_constrained.py

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+from pathlib import Path
+
+import pytest
+import torch
+import torch.nn as nn
+from flwr.common.typing import Config
+from torch.nn.modules.loss import _Loss
+from torch.optim import Optimizer
+from torch.utils.data import DataLoader, TensorDataset
+
+from fl4health.clients.basic_client import BasicClient
+from fl4health.metrics import Accuracy
+from fl4health.mixins.adaptive_drift_constrained import AdaptiveDriftConstrainedMixin, AdaptiveDriftConstrainedProtocol
+from fl4health.mixins.core import BasicClientProtocol
+from fl4health.parameter_exchange.packing_exchanger import FullParameterExchangerWithPacking
+from fl4health.parameter_exchange.parameter_packer import (
+    ParameterPackerAdaptiveConstraint,
+)
+
+
+class _TestBasicClient(BasicClient):
+    def get_model(self, config: Config) -> nn.Module:
+        return self.model
+
+    def get_data_loaders(self, config: Config) -> tuple[DataLoader, ...]:
+        return self.train_loader, self.val_loader
+
+    def get_optimizer(self, config: Config) -> Optimizer | dict[str, Optimizer]:
+        return self.optimizers["global"]
+
+    def get_criterion(self, config: Config) -> _Loss:
+        return torch.nn.CrossEntropyLoss()
+
+
+class _TestAdaptedClient(AdaptiveDriftConstrainedMixin, _TestBasicClient):
+    pass
+
+
+class _InvalidTestAdaptedClient(AdaptiveDriftConstrainedMixin):
+    pass
+
+
+def test_init() -> None:
+    # setup client
+    client = _TestAdaptedClient(data_path=Path(""), metrics=[Accuracy()], device=torch.device("cpu"))
+    client.model = torch.nn.Linear(5, 5)
+    client.optimizers = {"global": torch.optim.SGD(client.model.parameters(), lr=0.0001)}  # type: ignore
+    client.train_loader = DataLoader(TensorDataset(torch.ones((1000, 28, 28, 1)), torch.ones((1000))))  # type: ignore
+    client.val_loader = DataLoader(TensorDataset(torch.ones((1000, 28, 28, 1)), torch.ones((1000))))  # type: ignore
+    client.parameter_exchanger = FullParameterExchangerWithPacking(ParameterPackerAdaptiveConstraint())
+    client.initialized = True
+    client.setup_client({})
+
+    assert isinstance(client, BasicClientProtocol)
+    assert isinstance(client, AdaptiveDriftConstrainedProtocol)
+
+
+def test_init_raises_value_error_when_basic_client_protocol_not_satisfied() -> None:
+    with pytest.raises(RuntimeError, match="This object needs to satisfy `BasicClientProtocolPreSetup`."):
+
+        _InvalidTestAdaptedClient(data_path=Path(""), metrics=[Accuracy()])