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Use class to organize distributed tests in modules #3069

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Sep 24, 2023
Merged

Use class to organize distributed tests in modules #3069

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20 changes: 10 additions & 10 deletions ignite/engine/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ def _prepare_batch(
def supervised_training_step(
model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
loss_fn: Union[Callable, torch.nn.Module],
loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module],
device: Optional[Union[str, torch.device]] = None,
non_blocking: bool = False,
prepare_batch: Callable = _prepare_batch,
Expand All @@ -57,7 +57,7 @@ def supervised_training_step(
Args:
model: the model to train.
optimizer: the optimizer to use.
loss_fn: the loss function to use.
loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor.
device: device type specification (default: None).
Applies to batches after starting the engine. Model *will not* be moved.
Device can be CPU, GPU.
Expand Down Expand Up @@ -120,7 +120,7 @@ def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[to
def supervised_training_step_amp(
model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
loss_fn: Union[Callable, torch.nn.Module],
loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module],
device: Optional[Union[str, torch.device]] = None,
non_blocking: bool = False,
prepare_batch: Callable = _prepare_batch,
Expand All @@ -134,7 +134,7 @@ def supervised_training_step_amp(
Args:
model: the model to train.
optimizer: the optimizer to use.
loss_fn: the loss function to use.
loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor.
device: device type specification (default: None).
Applies to batches after starting the engine. Model *will not* be moved.
Device can be CPU, GPU.
Expand Down Expand Up @@ -212,7 +212,7 @@ def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[to
def supervised_training_step_apex(
model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
loss_fn: Union[Callable, torch.nn.Module],
loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module],
device: Optional[Union[str, torch.device]] = None,
non_blocking: bool = False,
prepare_batch: Callable = _prepare_batch,
Expand All @@ -225,7 +225,7 @@ def supervised_training_step_apex(
Args:
model: the model to train.
optimizer: the optimizer to use.
loss_fn: the loss function to use.
loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor.
device: device type specification (default: None).
Applies to batches after starting the engine. Model *will not* be moved.
Device can be CPU, GPU.
Expand Down Expand Up @@ -295,7 +295,7 @@ def update(engine: Engine, batch: Sequence[torch.Tensor]) -> Union[Any, Tuple[to
def supervised_training_step_tpu(
model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
loss_fn: Union[Callable, torch.nn.Module],
loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module],
device: Optional[Union[str, torch.device]] = None,
non_blocking: bool = False,
prepare_batch: Callable = _prepare_batch,
Expand All @@ -308,7 +308,7 @@ def supervised_training_step_tpu(
Args:
model: the model to train.
optimizer: the optimizer to use.
loss_fn: the loss function to use.
loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor.
device: device type specification (default: None).
Applies to batches after starting the engine. Model *will not* be moved.
Device can be CPU, TPU.
Expand Down Expand Up @@ -404,7 +404,7 @@ def _check_arg(
def create_supervised_trainer(
model: torch.nn.Module,
optimizer: torch.optim.Optimizer,
loss_fn: Union[Callable, torch.nn.Module],
loss_fn: Union[Callable[[Any, Any], torch.Tensor], torch.nn.Module],
device: Optional[Union[str, torch.device]] = None,
non_blocking: bool = False,
prepare_batch: Callable = _prepare_batch,
Expand All @@ -420,7 +420,7 @@ def create_supervised_trainer(
Args:
model: the model to train.
optimizer: the optimizer to use.
loss_fn: the loss function to use.
loss_fn: the loss function that receives `y_pred` and `y`, and returns the loss as a tensor.
device: device type specification (default: None).
Applies to batches after starting the engine. Model *will not* be moved.
Device can be CPU, GPU or TPU.
Expand Down
1 change: 1 addition & 0 deletions tests/ignite/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -397,6 +397,7 @@ def gloo_hvd_executor():
],
),
],
scope="class",
)
def distributed(request, local_rank, world_size):
if request.param in ("nccl", "gloo_cpu", "gloo"):
Expand Down