Factor out methods (#10215)

src/transformers/modeling_utils.py

@@ -86,6 +86,36 @@ def find_pruneable_heads_and_indices(
     return heads, index
 
 
+def get_parameter_device(parameter: Union[nn.Module, GenerationMixin, "ModuleUtilsMixin"]):
+    try:
+        return next(parameter.parameters()).device
+    except StopIteration:
+        # For nn.DataParallel compatibility in PyTorch 1.5
+
+        def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
+            tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
+            return tuples
+
+        gen = parameter._named_members(get_members_fn=find_tensor_attributes)
+        first_tuple = next(gen)
+        return first_tuple[1].device
+
+
+def get_parameter_dtype(parameter: Union[nn.Module, GenerationMixin, "ModuleUtilsMixin"]):
+    try:
+        return next(parameter.parameters()).dtype
+    except StopIteration:
+        # For nn.DataParallel compatibility in PyTorch 1.5
+
+        def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
+            tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
+            return tuples
+
+        gen = parameter._named_members(get_members_fn=find_tensor_attributes)
+        first_tuple = next(gen)
+        return first_tuple[1].dtype
+
+
 class ModuleUtilsMixin:
     """
     A few utilities for :obj:`torch.nn.Modules`, to be used as a mixin.
@@ -145,36 +175,14 @@ class ModuleUtilsMixin:
         :obj:`torch.device`: The device on which the module is (assuming that all the module parameters are on the same
         device).
         """
-        try:
-            return next(self.parameters()).device
-        except StopIteration:
-            # For nn.DataParallel compatibility in PyTorch 1.5
-
-            def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-                tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
-                return tuples
-
-            gen = self._named_members(get_members_fn=find_tensor_attributes)
-            first_tuple = next(gen)
-            return first_tuple[1].device
+        return get_parameter_device(self)
 
     @property
     def dtype(self) -> dtype:
         """
         :obj:`torch.dtype`: The dtype of the module (assuming that all the module parameters have the same dtype).
         """
-        try:
-            return next(self.parameters()).dtype
-        except StopIteration:
-            # For nn.DataParallel compatibility in PyTorch 1.5
-
-            def find_tensor_attributes(module: nn.Module) -> List[Tuple[str, Tensor]]:
-                tuples = [(k, v) for k, v in module.__dict__.items() if torch.is_tensor(v)]
-                return tuples
-
-            gen = self._named_members(get_members_fn=find_tensor_attributes)
-            first_tuple = next(gen)
-            return first_tuple[1].dtype
+        return get_parameter_dtype(self)
 
     def invert_attention_mask(self, encoder_attention_mask: Tensor) -> Tensor:
         """
@@ -1238,7 +1246,7 @@ class PoolerStartLogits(nn.Module):
         x = self.dense(hidden_states).squeeze(-1)
 
         if p_mask is not None:
-            if next(self.parameters()).dtype == torch.float16:
+            if get_parameter_dtype(self) == torch.float16:
                 x = x * (1 - p_mask) - 65500 * p_mask
             else:
                 x = x * (1 - p_mask) - 1e30 * p_mask
@@ -1305,7 +1313,7 @@ class PoolerEndLogits(nn.Module):
         x = self.dense_1(x).squeeze(-1)
 
         if p_mask is not None:
-            if next(self.parameters()).dtype == torch.float16:
+            if get_parameter_dtype(self) == torch.float16:
                 x = x * (1 - p_mask) - 65500 * p_mask
             else:
                 x = x * (1 - p_mask) - 1e30 * p_mask