Refactor data-related interfaces & add interfaces for trainer and worker

README.md

@@ -162,13 +162,13 @@ Note that FederatedScope provides a unified interface for both standalone mode a
 
 The standalone mode in FederatedScope means to simulate multiple participants (servers and clients) in a single device, while participants' data are isolated from each other and their models might be shared via message passing. 
 
-Here we demonstrate how to run a standard FL task with FederatedScope, with setting `cfg.data.type = 'FEMNIST'`and `cfg.model.type = 'ConvNet2'` to run vanilla FedAvg for an image classification task. Users can customize training configurations, such as `cfg.federated.total_round_num`, `cfg.data.batch_size`, and `cfg.train.optimizer.lr`, in the configuration (a .yaml file), and run a standard FL task as: 
+Here we demonstrate how to run a standard FL task with FederatedScope, with setting `cfg.data.type = 'FEMNIST'`and `cfg.model.type = 'ConvNet2'` to run vanilla FedAvg for an image classification task. Users can customize training configurations, such as `cfg.federated.total_round_num`, `cfg.dataloader.batch_size`, and `cfg.train.optimizer.lr`, in the configuration (a .yaml file), and run a standard FL task as: 
 
 ```bash
 # Run with default configurations
 python federatedscope/main.py --cfg scripts/example_configs/femnist.yaml
 # Or with custom configurations
-python federatedscope/main.py --cfg scripts/example_configs/femnist.yaml federate.total_round_num 50 data.batch_size 128
+python federatedscope/main.py --cfg scripts/example_configs/femnist.yaml federate.total_round_num 50 dataloader.batch_size 128
 ```
 
 Then you can observe some monitored metrics during the training process as:

environment/extra_dependencies_torch1.10-application.sh

@@ -9,3 +9,6 @@ conda install -y nltk
 conda install -y sentencepiece textgrid typeguard -c conda-forge
 conda install -y transformers==4.16.2 tokenizers==0.10.3 datasets -c huggingface -c conda-forge
 conda install -y torchtext -c pytorch
+
+# Tabular
+conda install -y openml==0.12.2

federatedscope/__init__.py

@@ -1,6 +1,6 @@
 from __future__ import absolute_import, division, print_function
 
-__version__ = '0.2.0'
+__version__ = '0.2.1'
 
 
 def _setup_logger():

federatedscope/attack/auxiliary/poisoning_data.py

@@ -50,9 +50,9 @@ def load_poisoned_dataset_edgeset(data, ctx, mode):
                 poison_testset.append((transforms_funcs(sample), label))
             data['poison_' + mode] = DataLoader(
                 poison_testset,
-                batch_size=ctx.data.batch_size,
+                batch_size=ctx.dataloader.batch_size,
                 shuffle=False,
-                num_workers=ctx.data.num_workers)
+                num_workers=ctx.dataloader.num_workers)
 
     elif "CIFAR10" in ctx.data.type:
         target_label = int(ctx.attack.target_label_ind)
@@ -91,9 +91,9 @@ def load_poisoned_dataset_edgeset(data, ctx, mode):
                 poison_testset.append((transforms_funcs(sample), label))
             data['poison_' + mode] = DataLoader(
                 poison_testset,
-                batch_size=ctx.data.batch_size,
+                batch_size=ctx.dataloader.batch_size,
                 shuffle=False,
-                num_workers=ctx.data.num_workers)
+                num_workers=ctx.dataloader.num_workers)
 
     else:
         raise RuntimeError(
@@ -213,9 +213,9 @@ def load_poisoned_dataset_pixel(data, ctx, mode):
             poisoned_dataset[iii] = (transforms_funcs(sample), label)
 
         data[mode] = DataLoader(poisoned_dataset,
-                                batch_size=ctx.data.batch_size,
+                                batch_size=ctx.dataloader.batch_size,
                                 shuffle=True,
-                                num_workers=ctx.data.num_workers)
+                                num_workers=ctx.dataloader.num_workers)
 
     if mode == MODE.TEST or mode == MODE.VAL:
         poisoned_dataset = addTrigger(data[mode].dataset,
@@ -234,10 +234,11 @@ def load_poisoned_dataset_pixel(data, ctx, mode):
             # (channel, height, width) = sample.shape #(c,h,w)
             poisoned_dataset[iii] = (transforms_funcs(sample), label)
 
-        data['poison_' + mode] = DataLoader(poisoned_dataset,
-                                            batch_size=ctx.data.batch_size,
-                                            shuffle=False,
-                                            num_workers=ctx.data.num_workers)
+        data['poison_' + mode] = DataLoader(
+            poisoned_dataset,
+            batch_size=ctx.dataloader.batch_size,
+            shuffle=False,
+            num_workers=ctx.dataloader.num_workers)
 
     return data
 

federatedscope/attack/trainer/MIA_invert_gradient_trainer.py

@@ -4,7 +4,7 @@
 import torch
 
 from federatedscope.core.trainers import GeneralTorchTrainer
-from federatedscope.core.auxiliaries.dataloader_builder import WrapDataset
+from federatedscope.core.data.wrap_dataset import WrapDataset
 from federatedscope.attack.auxiliary.MIA_get_target_data import get_target_data
 
 logger = logging.getLogger(__name__)

federatedscope/attack/trainer/benign_trainer.py

@@ -1,15 +1,8 @@
-from calendar import c
 import logging
 from typing import Type
-import torch
 import numpy as np
 
 from federatedscope.core.trainers import GeneralTorchTrainer
-from federatedscope.core.auxiliaries.transform_builder import get_transform
-from federatedscope.attack.auxiliary.backdoor_utils import normalize
-from federatedscope.core.auxiliaries.dataloader_builder import WrapDataset
-from federatedscope.core.auxiliaries.dataloader_builder import get_dataloader
-from federatedscope.core.auxiliaries.ReIterator import ReIterator
 
 logger = logging.getLogger(__name__)
 

federatedscope/contrib/data/example.py

@@ -1,7 +1,7 @@
 from federatedscope.register import register_data
 
 
-def MyData(config):
+def MyData(config, client_cfgs=None):
     r"""
     Returns:
             data:
@@ -17,12 +17,13 @@ def MyData(config):
     """
     data = None
     config = config
+    client_cfgs = client_cfgs
     return data, config
 
 
-def call_my_data(config):
+def call_my_data(config, client_cfgs):
     if config.data.type == "mydata":
-        data, modified_config = MyData(config)
+        data, modified_config = MyData(config, client_cfgs)
         return data, modified_config
 
 

federatedscope/contrib/trainer/torch_example.py

@@ -0,0 +1,104 @@
+import inspect
+from federatedscope.register import register_trainer
+from federatedscope.core.trainers import BaseTrainer
+
+# An example for converting torch training process to FS training process
+
+# Refer to `federatedscope.core.trainers.BaseTrainer` for interface.
+
+# Try with FEMNIST:
+#  python federatedscope/main.py --cfg scripts/example_configs/femnist.yaml \
+#  trainer.type mytorchtrainer federate.sample_client_rate 0.01 \
+#  federate.total_round_num 5 eval.best_res_update_round_wise_key test_loss
+
+
+class MyTorchTrainer(BaseTrainer):
+    def __init__(self, model, data, device, **kwargs):
+        import torch
+        # NN modules
+        self.model = model
+        # FS `ClientData` or your own data
+        self.data = data
+        # Device name
+        self.device = device
+        # kwargs
+        self.kwargs = kwargs
+        # Criterion & Optimizer
+        self.criterion = torch.nn.CrossEntropyLoss()
+        self.optimizer = torch.optim.SGD(self.model.parameters(),
+                                         lr=0.001,
+                                         momentum=0.9,
+                                         weight_decay=1e-4)
+
+    def train(self):
+        # _hook_on_fit_start_init
+        self.model.to(self.device)
+        self.model.train()
+
+        total_loss = num_samples = 0
+        # _hook_on_batch_start_init
+        for x, y in self.data['train']:
+            # _hook_on_batch_forward
+            x, y = x.to(self.device), y.to(self.device)
+            outputs = self.model(x)
+            loss = self.criterion(outputs, y)
+
+            # _hook_on_batch_backward
+            self.optimizer.zero_grad()
+            loss.backward()
+            self.optimizer.step()
+
+            # _hook_on_batch_end
+            total_loss += loss.item() * y.shape[0]
+            num_samples += y.shape[0]
+
+        # _hook_on_fit_end
+        return num_samples, self.model.cpu().state_dict(), \
+            {'loss_total': total_loss, 'avg_loss': total_loss/float(
+                num_samples)}
+
+    def evaluate(self, target_data_split_name='test'):
+        import torch
+        with torch.no_grad():
+            self.model.to(self.device)
+            self.model.eval()
+            total_loss = num_samples = 0
+            # _hook_on_batch_start_init
+            for x, y in self.data[target_data_split_name]:
+                # _hook_on_batch_forward
+                x, y = x.to(self.device), y.to(self.device)
+                pred = self.model(x)
+                loss = self.criterion(pred, y)
+
+                # _hook_on_batch_end
+                total_loss += loss.item() * y.shape[0]
+                num_samples += y.shape[0]
+
+            # _hook_on_fit_end
+            return {
+                f'{target_data_split_name}_loss': total_loss,
+                f'{target_data_split_name}_total': num_samples,
+                f'{target_data_split_name}_avg_loss': total_loss /
+                float(num_samples)
+            }
+
+    def update(self, model_parameters, strict=False):
+        self.model.load_state_dict(model_parameters, strict)
+
+    def get_model_para(self):
+        return self.model.cpu().state_dict()
+
+    def print_trainer_meta_info(self):
+        sign = inspect.signature(self.__init__).parameters.values()
+        meta_info = tuple([(val.name, getattr(self, val.name))
+                           for val in sign])
+        return f'{self.__class__.__name__}{meta_info}'
+
+
+def call_my_torch_trainer(trainer_type):
+    if trainer_type == 'mytorchtrainer':
+        trainer_builder = MyTorchTrainer
+        return trainer_builder
+
+
+register_trainer('mytorchtrainer', call_my_torch_trainer)