Cast to >=float32 tensor when passing scalar to self.log

src/lightning/pytorch/core/module.py

@@ -451,7 +451,7 @@ def log(
                 " but it should not contain information about `dataloader_idx`"
             )
 
-        value = apply_to_collection(value, (Tensor, numbers.Number), self.__to_tensor, name)
+        value = apply_to_collection(value, (Tensor, numbers.Number), self.__to_tensor_high_precision, name)
 
         if trainer._logger_connector.should_reset_tensors(self._current_fx_name):
             # if we started a new epoch (running its first batch) the hook name has changed
@@ -621,6 +621,21 @@ def __check_not_nested(value: dict, name: str) -> None:
     def __check_allowed(v: Any, name: str, value: Any) -> None:
         raise ValueError(f"`self.log({name}, {value})` was called, but `{type(v).__name__}` values cannot be logged")
 
+    def _get_default_high_precision_dtype() -> torch.dtype:
+        """The default dtype for new tensors, but no lower than float32."""
+        dtype = torch.get_default_dtype()
+        return dtype if dtype in (torch.float32, torch.float64) else torch.float32
+
+    def __to_tensor_high_precision(self, value: Union[Tensor, numbers.Number], name: str) -> Tensor:
+        value = value.clone().detach() if isinstance(value, Tensor) else torch.tensor(value, device=self.device, dtype=_get_default_high_precision_dtype())
+        if not torch.numel(value) == 1:
+            raise ValueError(
+                f"`self.log({name}, {value})` was called, but the tensor must have a single element."
+                f" You can try doing `self.log({name}, {value}.mean())`"
+            )
+        value = value.squeeze()
+        return value
+
     def __to_tensor(self, value: Union[Tensor, numbers.Number], name: str) -> Tensor:
         value = value.clone().detach() if isinstance(value, Tensor) else torch.tensor(value, device=self.device)
         if not torch.numel(value) == 1: