chore: rename use_gpu to disable_gpu in TorchAlgo

changes/241.changed

@@ -0,0 +1 @@
+BREAKING: change `use_gpu` to `diasble_gpu` in all `TorchAlgo`. The device is set to `cpu` is no GPU are available or if `disable_gpu` is set to `True`. You must inverse the boolean in your code to keep the same behaviour (`diasble_gpu == not use_gpu`).

substrafl/algorithms/pytorch/torch_base_algo.py

@@ -46,7 +46,7 @@ def __init__(
         optimizer: Optional[torch.optim.Optimizer] = None,
         scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
         seed: Optional[int] = None,
-        use_gpu: bool = True,
+        disable_gpu: bool = False,
         *args,
         **kwargs,
     ):
@@ -63,7 +63,7 @@ def __init__(
             np.random.seed(seed)
             torch.manual_seed(seed)
 
-        self._device = self._get_torch_device(use_gpu=use_gpu)
+        self._device = self._get_torch_device(disable_gpu=disable_gpu)
 
         self._model = model.to(self._device)
         self._optimizer = optimizer
@@ -212,18 +212,18 @@ def _local_train(
             if self._scheduler is not None:
                 self._scheduler.step()
 
-    def _get_torch_device(self, use_gpu: bool) -> torch.device:
+    def _get_torch_device(self, disable_gpu: bool) -> torch.device:
         """Get the torch device, CPU or GPU, depending
         on availability and user input.
 
         Args:
-            use_gpu (bool): whether to use GPUs if available or not.
+            disable_gpu (bool): whether to use GPUs if available or not.
 
         Returns:
             torch.device: Torch device
         """
         device = torch.device("cpu")
-        if use_gpu and torch.cuda.is_available():
+        if not disable_gpu and torch.cuda.is_available():
             device = torch.device("cuda")
         return device
 

substrafl/algorithms/pytorch/torch_fed_avg_algo.py

@@ -92,7 +92,7 @@ def __init__(
         scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
         with_batch_norm_parameters: bool = False,
         seed: Optional[int] = None,
-        use_gpu: bool = True,
+        disable_gpu: bool = False,
         *args,
         **kwargs,
     ):
@@ -125,7 +125,8 @@ def __init__(
             with_batch_norm_parameters (bool): Whether to include the batch norm layer parameters in the fed avg
                 strategy. Defaults to False.
             seed (typing.Optional[int]): Seed set at the algo initialization on each organization. Defaults to None.
-            use_gpu (bool): Whether to use the GPUs if they are available. Defaults to True.
+            disable_gpu (bool): Force disabling GPU usage. If False, GPU will used if available, else CPU will be used.
+                Defaults to False.
         """
         super().__init__(
             *args,
@@ -136,7 +137,7 @@ def __init__(
             dataset=dataset,
             scheduler=scheduler,
             seed=seed,
-            use_gpu=use_gpu,
+            disable_gpu=disable_gpu,
             **kwargs,
         )
         self._with_batch_norm_parameters = with_batch_norm_parameters

substrafl/algorithms/pytorch/torch_fed_pca_algo.py

@@ -87,7 +87,7 @@ def __init__(
         out_features: int,
         batch_size: Optional[int] = None,
         seed: int = 1,
-        use_gpu: bool = True,
+        disable_gpu: bool = False,
         *args,
         **kwargs,
     ):
@@ -101,7 +101,7 @@ def __init__(
             out_features (int): dimension to keep after PCA
             batch_size (Optional[int]): mini-batch size
             seed (int): random generator seed. The seed is mandatory. Default to 1.
-            use_gpu (bool): whether to use GPU or not. Default to True.
+            disable_gpu (bool): force to disable GPUs usage. Defaults to False.
         """
         self.in_features = in_features
         self.out_features = out_features
@@ -122,7 +122,7 @@ def __init__(
             index_generator=None,
             dataset=dataset,
             seed=self._seed,
-            use_gpu=use_gpu,
+            disable_gpu=disable_gpu,
             **kwargs,
         )
 

substrafl/algorithms/pytorch/torch_newton_raphson_algo.py

@@ -48,7 +48,7 @@ def __init__(
         l2_coeff: float = 0,
         with_batch_norm_parameters: bool = False,
         seed: Optional[int] = None,
-        use_gpu: bool = True,
+        disable_gpu: bool = False,
         *args,
         **kwargs,
     ):
@@ -80,7 +80,8 @@ def __init__(
             with_batch_norm_parameters (bool): Whether to include the batch norm layer parameters in the Newton-Raphson
                 strategy. Defaults to False.
             seed (typing.Optional[int]): Seed set at the algo initialization on each organization. Defaults to None.
-            use_gpu (bool): Whether to use the GPUs if they are available. Defaults to True.
+            disable_gpu (bool): Force disabling GPU usage. If False, GPU will used if available, else CPU will be used.
+                Defaults to False.
         """
         assert "optimizer" not in kwargs, "Newton Raphson strategy does not uses optimizers"
 
@@ -91,7 +92,7 @@ def __init__(
             optimizer=None,
             index_generator=None,
             dataset=dataset,
-            use_gpu=use_gpu,
+            disable_gpu=disable_gpu,
             seed=seed,
             **kwargs,
         )

substrafl/algorithms/pytorch/torch_scaffold_algo.py

@@ -118,7 +118,7 @@ def __init__(
         with_batch_norm_parameters: bool = False,
         c_update_rule: CUpdateRule = CUpdateRule.FAST,
         seed: Optional[int] = None,
-        use_gpu: bool = True,
+        disable_gpu: bool = False,
         *args,
         **kwargs,
     ):
@@ -153,7 +153,8 @@ def __init__(
                 client control variate.
                 Defaults to CUpdateRule.FAST.
             seed (typing.Optional[int]): Seed set at the algo initialization on each organization. Defaults to None.
-            use_gpu (bool): Whether to use the GPUs if they are available. Defaults to True.
+            disable_gpu (bool): Force disabling GPU usage. If False, GPU will used if available, else CPU will be used.
+                Defaults to False.
         Raises:
             :ref:`~substrafl.exceptions.NumUpdatesValueError`: If `num_updates` is inferior or equal to zero.
         """
@@ -165,7 +166,7 @@ def __init__(
             index_generator=index_generator,
             dataset=dataset,
             scheduler=scheduler,
-            use_gpu=use_gpu,
+            disable_gpu=disable_gpu,
             seed=seed,
             **kwargs,
         )

substrafl/algorithms/pytorch/torch_single_organization_algo.py

@@ -90,7 +90,7 @@ def __init__(
         dataset: torch.utils.data.Dataset,
         scheduler: Optional[torch.optim.lr_scheduler._LRScheduler] = None,
         seed: Optional[int] = None,
-        use_gpu: bool = True,
+        disable_gpu: bool = False,
         *args,
         **kwargs,
     ):
@@ -121,7 +121,8 @@ def __init__(
             scheduler (torch.optim.lr_scheduler._LRScheduler, Optional): A torch scheduler that will be called at every
                 batch. If None, no scheduler will be used. Defaults to None.
             seed (typing.Optional[int]): Seed set at the algo initialization on each organization. Defaults to None.
-            use_gpu (bool): Whether to use the GPUs if they are available. Defaults to True.
+            disable_gpu (bool): Force disabling GPU usage. If False, GPU will used if available, else CPU will be used.
+                Defaults to False.
         """
         super().__init__(
             *args,
@@ -132,7 +133,7 @@ def __init__(
             dataset=dataset,
             scheduler=scheduler,
             seed=seed,
-            use_gpu=use_gpu,
+            disable_gpu=disable_gpu,
             **kwargs,
         )
 

tests/algorithms/pytorch/test_base_algo.py

@@ -50,7 +50,6 @@ def __init__(self):
                 optimizer=torch.optim.SGD(perceptron.parameters(), lr=0.1),
                 index_generator=nig,
                 seed=test_seed,
-                use_gpu=False,
             )
 
         @property
@@ -161,8 +160,8 @@ def train(self, data_from_opener, shared_state):
     return MyAlgo
 
 
-@pytest.fixture(params=[pytest.param(True, marks=pytest.mark.gpu), False])
-def use_gpu(request):
+@pytest.fixture(params=[pytest.param(False, marks=pytest.mark.gpu), True])
+def disable_gpu(request):
     return request.param
 
 
@@ -174,7 +173,7 @@ def use_gpu(request):
         (TorchNewtonRaphsonAlgo, NewtonRaphson),
     ]
 )
-def dummy_gpu(request, torch_linear_model, use_gpu, numpy_torch_dataset):
+def dummy_gpu(request, torch_linear_model, disable_gpu, numpy_torch_dataset):
     nig = NpIndexGenerator(
         batch_size=1,
         num_updates=1,
@@ -189,7 +188,7 @@ def __init__(self):
                     criterion=torch.nn.MSELoss(),
                     dataset=numpy_torch_dataset,
                     batch_size=1,
-                    use_gpu=use_gpu,
+                    disable_gpu=disable_gpu,
                 )
             else:
                 super().__init__(
@@ -198,18 +197,18 @@ def __init__(self):
                     criterion=torch.nn.MSELoss(),
                     dataset=numpy_torch_dataset,
                     index_generator=nig,
-                    use_gpu=use_gpu,
+                    disable_gpu=disable_gpu,
                 )
-            if use_gpu:
-                assert self._device == torch.device("cuda")
-            else:
+            if disable_gpu or not torch.cuda.is_available():
                 assert self._device == torch.device("cpu")
+            else:
+                assert self._device == torch.device("cuda")
 
         @property
         def strategies(self):
             return list(StrategyName)
 
-    return MyAlgo, request.param[1], use_gpu
+    return MyAlgo, request.param[1], disable_gpu
 
 
 def test_base_algo_custom_init_arg_default_value(session_dir, dummy_algo_custom_init_arg):
@@ -469,7 +468,7 @@ def test_gpu(
     aggregation_node,
 ):
     num_rounds = 2
-    algo_class, strategy_class, use_gpu = dummy_gpu
+    algo_class, strategy_class, disable_gpu = dummy_gpu
     my_algo = algo_class()
     algo_deps = Dependency(
         pypi_dependencies=["torch==2.2.1", "numpy==1.26.4", "pytest"],
@@ -499,7 +498,8 @@ def test_gpu(
         dependencies=algo_deps,
         experiment_folder=session_dir / "experiment_folder",
         clean_models=False,
-        name=f'Testing the GPU - strategy {strategy_class.__name__}, running  on {"cuda" if use_gpu else "cpu"}',
+        name=f'Testing the GPU - strategy {strategy_class.__name__}, running  on \
+            {"cpu" if (disable_gpu or not torch.cuda.is_available()) else "cuda"}',
     )
 
     # Wait for the compute plan to be finished

tests/algorithms/pytorch/test_fed_avg.py

@@ -46,7 +46,6 @@ def __init__(
                 model=perceptron,
                 index_generator=nig,
                 dataset=numpy_torch_dataset,
-                use_gpu=False,
             )
 
     return MyAlgo

tests/algorithms/pytorch/test_fed_pca_algo.py

@@ -36,7 +36,6 @@ def __init__(self, batch_size=1):
                 batch_size=batch_size,
                 dataset=numpy_torch_dataset,
                 seed=seed,
-                use_gpu=False,
             )
 
     return MyAlgo

tests/algorithms/pytorch/test_newton_raphson.py

@@ -348,7 +348,6 @@ def __init__(self):
                 batch_size=BATCH_SIZE,
                 dataset=numpy_torch_dataset,
                 l2_coeff=0,
-                use_gpu=False,
             )
 
     my_algo = MyAlgo()
@@ -435,7 +434,6 @@ def __init__(self):
                 batch_size=BATCH_SIZE,
                 dataset=numpy_torch_dataset,
                 l2_coeff=0,
-                use_gpu=False,
             )
 
     my_algo = MyAlgo()

tests/algorithms/pytorch/test_scaffold.py

@@ -50,7 +50,6 @@ def __init__(
                 index_generator=nig,
                 dataset=numpy_torch_dataset,
                 scheduler=scheduler,
-                use_gpu=False,
             )
 
     return MyAlgo

tests/algorithms/pytorch/test_single_organization.py

@@ -50,7 +50,6 @@ def __init__(
                 model=perceptron,
                 index_generator=nig,
                 dataset=numpy_torch_dataset,
-                use_gpu=False,
             )
 
     my_algo = MySingleOrganizationAlgo()
@@ -107,7 +106,6 @@ def __init__(
                 model=perceptron,
                 index_generator=nig,
                 dataset=numpy_torch_dataset,
-                use_gpu=False,
             )
 
     my_algo = MySingleOrganizationAlgo()