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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
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import time
from abc import abstractmethod
import torch
from nvflare.apis.dxo import from_shareable
from nvflare.app_opt.p2p.executors.sync_executor import SyncAlgorithmExecutor
from nvflare.app_opt.p2p.utils.metrics import compute_loss_over_dataset
from nvflare.app_opt.p2p.utils.utils import get_device
[docs]
class DGDExecutor(SyncAlgorithmExecutor):
"""An executor that implements Stochastic Distributed Gradient Descent (DGD) in a peer-to-peer (P2P) learning setup.
Each client maintains its own local model and synchronously exchanges model parameters with its neighbors
at each iteration. The model parameters are updated based on the neighbors' parameters and local gradient descent steps.
The executor also tracks and records training, validation and test losses over time.
The number of iterations and the learning rate must be provided by the controller when assigning to run the algorithm.
They can be set in the extra parameters of the controller's config with the "iterations" and "stepsize" keys.
Note:
Subclasses must implement the __init__ method to initialize the model, loss function, and data loaders.
Args:
model (torch.nn.Module, optional): The neural network model used for training.
loss (torch.nn.modules.loss._Loss, optional): The loss function used for training.
train_dataloader (torch.utils.data.DataLoader, optional): DataLoader for the training dataset.
test_dataloader (torch.utils.data.DataLoader, optional): DataLoader for the testing dataset.
val_dataloader (torch.utils.data.DataLoader, optional): DataLoader for the validation dataset.
Attributes:
model (torch.nn.Module): The neural network model.
loss (torch.nn.modules.loss._Loss): The loss function.
train_dataloader (torch.utils.data.DataLoader): DataLoader for training data.
test_dataloader (torch.utils.data.DataLoader): DataLoader for testing data.
val_dataloader (torch.utils.data.DataLoader): DataLoader for validation data.
train_loss_sequence (list[tuple]): Records of training loss over time.
test_loss_sequence (list[tuple]): Records of testing loss over time.
"""
@abstractmethod
def __init__(
self,
model: torch.nn.Module | None = None,
loss: torch.nn.modules.loss._Loss | None = None,
train_dataloader: torch.utils.data.DataLoader | None = None,
test_dataloader: torch.utils.data.DataLoader | None = None,
val_dataloader: torch.utils.data.DataLoader | None = None,
):
super().__init__()
self.device = get_device()
self.model = model.to(self.device)
self.loss = loss.to(self.device)
self.train_dataloader = train_dataloader
self.test_dataloader = test_dataloader
self.val_dataloader = val_dataloader
# metrics
self.train_loss_sequence = []
self.test_loss_sequence = []
[docs]
def run_algorithm(self, fl_ctx, shareable, abort_signal):
start_time = time.time()
iter_dataloader = iter(self.train_dataloader)
for iteration in range(self._iterations):
self.log_info(fl_ctx, f"iteration: {iteration}/{self._iterations}")
if abort_signal.triggered:
break
try:
data, label = next(iter_dataloader)
data, label = data.to(self.device), label.to(self.device)
except StopIteration:
# 3. store metrics
current_time = time.time() - start_time
self.train_loss_sequence.append(
(
current_time,
compute_loss_over_dataset(
self.model,
self.loss,
self.train_dataloader,
device=self.device,
),
)
)
self.test_loss_sequence.append(
(
current_time,
compute_loss_over_dataset(
self.model,
self.loss,
self.test_dataloader,
device=self.device,
),
)
)
# restart after an epoch
iter_dataloader = iter(self.train_dataloader)
data, label = next(iter_dataloader)
data, label = data.to(self.device), label.to(self.device)
# run algorithm step
# 1. exchange values
with torch.no_grad():
self._exchange_values(fl_ctx, value=self.model.parameters(), iteration=iteration)
# compute consensus value
for idx, param in enumerate(self.model.parameters()):
if param.requires_grad:
param.mul_(self._weight)
for neighbor in self.neighbors:
neighbor_param = self.neighbors_values[iteration][neighbor.id][idx].to(self.device)
param.add_(
neighbor_param,
alpha=neighbor.weight,
)
# 2. update current value
self.model.zero_grad()
pred = self.model(data)
loss = self.loss(pred, label)
loss.backward()
with torch.no_grad():
for param in self.model.parameters():
if param.grad is not None:
param.add_(param.grad, alpha=-self._stepsize)
# free memory that's no longer needed
del self.neighbors_values[iteration]
def _to_message(self, x):
return [param.cpu().numpy() for param in iter(x) if param.requires_grad]
def _from_message(self, x):
return [torch.from_numpy(param) for param in x]
def _pre_algorithm_run(self, fl_ctx, shareable, abort_signal):
self._iterations = from_shareable(shareable).data["iterations"]
self._stepsize = from_shareable(shareable).data["stepsize"]
init_train_loss = compute_loss_over_dataset(self.model, self.loss, self.train_dataloader, device=self.device)
init_test_loss = compute_loss_over_dataset(self.model, self.loss, self.test_dataloader, device=self.device)
self.train_loss_sequence.append((0, init_train_loss))
self.test_loss_sequence.append((0, init_test_loss))
def _post_algorithm_run(self, *args, **kwargs):
torch.save(torch.tensor(self.train_loss_sequence), "train_loss_sequence.pt")
torch.save(torch.tensor(self.test_loss_sequence), "test_loss_sequence.pt")