refactor(framework) Update huggingface template for flwr new

src/py/flwr/cli/new/templates/app/code/client.huggingface.py.tpl

@@ -1,18 +1,11 @@
 """$project_name: A Flower / $framework_str app."""
 
+import torch
 from flwr.client import ClientApp, NumPyClient
 from flwr.common import Context
 from transformers import AutoModelForSequenceClassification
 
-from $import_name.task import (
-    get_weights,
-    load_data,
-    set_weights,
-    train,
-    test,
-    CHECKPOINT,
-    DEVICE,
-)
+from $import_name.task import get_weights, load_data, set_weights, test, train
 
 
 # Flower client
@@ -22,37 +15,34 @@ class FlowerClient(NumPyClient):
         self.trainloader = trainloader
         self.testloader = testloader
         self.local_epochs = local_epochs
-
-    def get_parameters(self, config):
-        return get_weights(self.net)
-
-    def set_parameters(self, parameters):
-        set_weights(self.net, parameters)
+        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+        self.net.to(self.device)
 
     def fit(self, parameters, config):
-        self.set_parameters(parameters)
-        train(
-            self.net,
-            self.trainloader,
-            epochs=self.local_epochs,
-        )
-        return self.get_parameters(config={}), len(self.trainloader), {}
+        set_weights(self.net, parameters)
+        train(self.net, self.trainloader, epochs=self.local_epochs, device=self.device)
+        return get_weights(self.net), len(self.trainloader), {}
 
     def evaluate(self, parameters, config):
-        self.set_parameters(parameters)
-        loss, accuracy = test(self.net, self.testloader)
+        set_weights(self.net, parameters)
+        loss, accuracy = test(self.net, self.testloader, self.device)
         return float(loss), len(self.testloader), {"accuracy": accuracy}
 
 
 def client_fn(context: Context):
-    # Load model and data
-    net = AutoModelForSequenceClassification.from_pretrained(
-        CHECKPOINT, num_labels=2
-    ).to(DEVICE)
 
+    # Get this client's dataset partition
     partition_id = context.node_config["partition-id"]
     num_partitions = context.node_config["num-partitions"]
-    trainloader, valloader = load_data(partition_id, num_partitions)
+    model_name = context.run_config["model-name"]
+    trainloader, valloader = load_data(partition_id, num_partitions, model_name)
+
+    # Load model
+    num_labels = context.run_config["num-labels"]
+    net = AutoModelForSequenceClassification.from_pretrained(
+        model_name, num_labels=num_labels
+    )
+
     local_epochs = context.run_config["local-epochs"]
 
     # Return Client instance

src/py/flwr/cli/new/templates/app/code/server.huggingface.py.tpl

@@ -1,18 +1,33 @@
 """$project_name: A Flower / $framework_str app."""
 
-from flwr.common import Context
-from flwr.server.strategy import FedAvg
+from flwr.common import Context, ndarrays_to_parameters
 from flwr.server import ServerApp, ServerAppComponents, ServerConfig
+from flwr.server.strategy import FedAvg
+from transformers import AutoModelForSequenceClassification
+
+from $import_name.task import get_weights
 
 
 def server_fn(context: Context):
     # Read from config
     num_rounds = context.run_config["num-server-rounds"]
+    fraction_fit = context.run_config["fraction-fit"]
+
+    # Initialize global model
+    model_name = context.run_config["model-name"]
+    num_labels = context.run_config["num-labels"]
+    net = AutoModelForSequenceClassification.from_pretrained(
+        model_name, num_labels=num_labels
+    )
+
+    weights = get_weights(net)
+    initial_parameters = ndarrays_to_parameters(weights)
 
     # Define strategy
     strategy = FedAvg(
-        fraction_fit=1.0,
+        fraction_fit=fraction_fit,
         fraction_evaluate=1.0,
+        initial_parameters=initial_parameters,
     )
     config = ServerConfig(num_rounds=num_rounds)
 

src/py/flwr/cli/new/templates/app/code/task.huggingface.py.tpl

@@ -4,24 +4,25 @@ import warnings
 from collections import OrderedDict
 
 import torch
+import transformers
+from datasets.utils.logging import disable_progress_bar
 from evaluate import load as load_metric
+from flwr_datasets import FederatedDataset
+from flwr_datasets.partitioner import IidPartitioner
 from torch.optim import AdamW
 from torch.utils.data import DataLoader
 from transformers import AutoTokenizer, DataCollatorWithPadding
 
-from flwr_datasets import FederatedDataset
-from flwr_datasets.partitioner import IidPartitioner
-
-
 warnings.filterwarnings("ignore", category=UserWarning)
-DEVICE = torch.device("cpu")
-CHECKPOINT = "distilbert-base-uncased"  # transformer model checkpoint
+warnings.filterwarnings("ignore", category=FutureWarning)
+disable_progress_bar()
+transformers.logging.set_verbosity_error()
 
 
 fds = None  # Cache FederatedDataset
 
 
-def load_data(partition_id: int, num_partitions: int):
+def load_data(partition_id: int, num_partitions: int, model_name: str):
     """Load IMDB data (training and eval)"""
     # Only initialize `FederatedDataset` once
     global fds
@@ -35,10 +36,12 @@ def load_data(partition_id: int, num_partitions: int):
     # Divide data: 80% train, 20% test
     partition_train_test = partition.train_test_split(test_size=0.2, seed=42)
 
-    tokenizer = AutoTokenizer.from_pretrained(CHECKPOINT)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
 
     def tokenize_function(examples):
-        return tokenizer(examples["text"], truncation=True)
+        return tokenizer(
+            examples["text"], truncation=True, add_special_tokens=True, max_length=512
+        )
 
     partition_train_test = partition_train_test.map(tokenize_function, batched=True)
     partition_train_test = partition_train_test.remove_columns("text")
@@ -59,25 +62,25 @@ def load_data(partition_id: int, num_partitions: int):
     return trainloader, testloader
 
 
-def train(net, trainloader, epochs):
+def train(net, trainloader, epochs, device):
     optimizer = AdamW(net.parameters(), lr=5e-5)
     net.train()
     for _ in range(epochs):
         for batch in trainloader:
-            batch = {k: v.to(DEVICE) for k, v in batch.items()}
+            batch = {k: v.to(device) for k, v in batch.items()}
             outputs = net(**batch)
             loss = outputs.loss
             loss.backward()
             optimizer.step()
             optimizer.zero_grad()
 
 
-def test(net, testloader):
+def test(net, testloader, device):
     metric = load_metric("accuracy")
     loss = 0
     net.eval()
     for batch in testloader:
-        batch = {k: v.to(DEVICE) for k, v in batch.items()}
+        batch = {k: v.to(device) for k, v in batch.items()}
         with torch.no_grad():
             outputs = net(**batch)
         logits = outputs.logits

src/py/flwr/cli/new/templates/app/pyproject.huggingface.toml.tpl

@@ -8,7 +8,7 @@ version = "1.0.0"
 description = ""
 license = "Apache-2.0"
 dependencies = [
-    "flwr[simulation]>=1.10.0",
+    "flwr[simulation]>=1.11.0",
     "flwr-datasets>=0.3.0",
     "torch==2.2.1",
     "transformers>=4.30.0,<5.0",
@@ -29,10 +29,18 @@ clientapp = "$import_name.client_app:app"
 
 [tool.flwr.app.config]
 num-server-rounds = 3
+fraction-fit = 0.5
 local-epochs = 1
+model-name = "prajjwal1/bert-tiny" # Set a larger model if you have access to more GPU resources
+num-labels = 2
 
 [tool.flwr.federations]
 default = "localhost"
 
 [tool.flwr.federations.localhost]
 options.num-supernodes = 10
+
+[tool.flwr.federations.localhost-gpu]
+options.num-supernodes = 10
+options.backend.client-resources.num-cpus = 4 # each ClientApp assumes to use 4CPUs
+options.backend.client-resources.num-gpus = 0.25 # at most 4 ClientApps will run in a given GPU