Make device and dtype required (#9168)

pytorch_lightning/utilities/apply_func.py

@@ -23,7 +23,6 @@
 import numpy as np
 import torch
 
-from pytorch_lightning.utilities.exceptions import MisconfigurationException
 from pytorch_lightning.utilities.imports import _compare_version, _TORCHTEXT_AVAILABLE
 
 if _TORCHTEXT_AVAILABLE:
@@ -36,22 +35,16 @@
 
 
 def to_dtype_tensor(
-    value: Union[int, float, List[Union[int, float]]],
-    dtype: Optional[torch.dtype] = None,
-    device: Union[str, torch.device] = None,
+    value: Union[int, float, List[Union[int, float]]], dtype: torch.dtype, device: Union[str, torch.device]
 ) -> torch.Tensor:
-    if device is None:
-        raise MisconfigurationException("device (torch.device) should be provided.")
     return torch.tensor(value, dtype=dtype, device=device)
 
 
-def from_numpy(value: np.ndarray, device: Union[str, torch.device] = None) -> torch.Tensor:
-    if device is None:
-        raise MisconfigurationException("device (torch.device) should be provided.")
+def from_numpy(value: np.ndarray, device: Union[str, torch.device]) -> torch.Tensor:
     return torch.from_numpy(value).to(device)
 
 
-CONVERSION_DTYPES: List[Tuple[Any, Callable[[Any], torch.Tensor]]] = [
+CONVERSION_DTYPES: List[Tuple[Any, Callable[[Any, Any], torch.Tensor]]] = [
     # bool -> uint8 as bool -> torch.bool triggers RuntimeError: Unsupported data type for NCCL process group
     (bool, partial(to_dtype_tensor, dtype=torch.uint8)),
     (int, partial(to_dtype_tensor, dtype=torch.int)),
@@ -276,9 +269,6 @@ def batch_to(data: Any) -> Any:
 
 
 def convert_to_tensors(data: Any, device: Union[str, torch.device]) -> Any:
-    if device is None:
-        raise MisconfigurationException("`torch.device` should be provided.")
-
     for src_dtype, conversion_func in CONVERSION_DTYPES:
         data = apply_to_collection(data, src_dtype, conversion_func, device=device)