Add support for list of tensors output in Layer Gradient Attributor (#1629)

Summary:
Pull Request resolved: #1629

As part of increasing % of layers supported, we want to extend Layer Gradient Attributor to support List of Tensor with grad outputs. This is fairly safe since we already support Tuple of Tensor with grad outputs, and iteration across both types behaves similarly.

Reviewed By: craymichael

Differential Revision: D79134965

Author: Sharon Tan

captum/_utils/common.py

@@ -759,10 +759,10 @@ def _reduce_list(
     Applies reduction function to given list. If each element in the list is
     a Tensor, applies reduction function to all elements of the list, and returns
     the output Tensor / value. If each element is a boolean, apply any method (or).
-    If each element is a tuple, applies reduction
-    function to corresponding elements of each tuple in the list, and returns
+    If each element is a tuple/list, applies reduction
+    function to corresponding elements of each tuple/list in the list, and returns
     tuple of reduction function outputs with length matching the length of tuple
-    val_list[0]. It is assumed that all tuples in the list have the same length
+    val_list[0]. It is assumed that all tuples/lists in the list have the same length
     and red_func can be applied to all elements in each corresponding position.
     """
     assert len(val_list) > 0, "Cannot reduce empty list!"
@@ -774,7 +774,7 @@ def _reduce_list(
     elif isinstance(val_list[0], bool):
         # pyre-fixme[7]: Expected `TupleOrTensorOrBoolGeneric` but got `bool`.
         return any(val_list)
-    elif isinstance(val_list[0], tuple):
+    elif isinstance(val_list[0], (tuple, list)):
         final_out = []
         # pyre-fixme[6]: For 1st argument expected `pyre_extensions.ReadOnly[Sized]`
         #  but got `TupleOrTensorOrBoolGeneric`.
@@ -786,7 +786,7 @@ def _reduce_list(
     else:
         raise AssertionError(
             "Elements to be reduced can only be"
-            "either Tensors or tuples containing Tensors."
+            "either Tensors or tuples/lists containing Tensors."
         )
     # pyre-fixme[7]: Expected `TupleOrTensorOrBoolGeneric` but got `Tuple[Any, ...]`.
     return tuple(final_out)

captum/_utils/gradient.py

@@ -46,8 +46,8 @@ def apply_gradient_requirements(
      a tensor originally required grad is returned.
     """
     assert isinstance(
-        inputs, tuple
-    ), "Inputs should be wrapped in a tuple prior to preparing for gradients"
+        inputs, (tuple, list)
+    ), "Inputs should be wrapped in a tuple or list prior to preparing for gradients"
     grad_required = []
     for index, input in enumerate(inputs):
         assert isinstance(input, torch.Tensor), "Given input is not a torch.Tensor"
@@ -298,9 +298,9 @@ def hook_wrapper(original_module):
         # pyre-fixme[2]: Parameter must be annotated.
         def forward_hook(module, inp, out=None):
             eval_tsrs = inp if attribute_to_layer_input else out
-            is_eval_tuple = isinstance(eval_tsrs, tuple)
+            is_eval_tuple_or_list = isinstance(eval_tsrs, (tuple, list))
 
-            if not is_eval_tuple:
+            if not is_eval_tuple_or_list:
                 eval_tsrs = (eval_tsrs,)
             if require_layer_grads:
                 apply_gradient_requirements(eval_tsrs, warn=False)
@@ -310,11 +310,16 @@ def forward_hook(module, inp, out=None):
                 # otherwise `backward()` on the last output layer won't execute.
                 if forward_hook_with_return:
                     saved_layer[original_module][eval_tsrs[0].device] = eval_tsrs
-                    eval_tsrs_to_return = tuple(
-                        eval_tsr.clone() for eval_tsr in eval_tsrs
-                    )
-                    if not is_eval_tuple:
-                        eval_tsrs_to_return = eval_tsrs_to_return[0]
+                    if not is_eval_tuple_or_list:
+                        eval_tsrs_to_return = eval_tsrs[0].clone()
+                    elif isinstance(eval_tsrs, list):
+                        eval_tsrs_to_return = [
+                            eval_tsr.clone() for eval_tsr in eval_tsrs
+                        ]
+                    else:
+                        eval_tsrs_to_return = tuple(
+                            eval_tsr.clone() for eval_tsr in eval_tsrs
+                        )
                     return eval_tsrs_to_return
                 else:
                     saved_layer[original_module][eval_tsrs[0].device] = tuple(

captum/testing/helpers/basic_models.py

@@ -432,6 +432,8 @@ def __init__(
         self.linear3.weight = nn.Parameter(torch.ones(2, 4))
         self.linear3.bias = nn.Parameter(torch.tensor([-1.0, 1.0]))
 
+        self.list_output_layer = PassThroughLayerOutput()
+
         self.int_layer = PassThroughLayerOutput()  # sample layer with an int output
 
     @no_type_check
@@ -452,11 +454,13 @@ def forward(
 
         relu_out = self.relu(lin1_out)
         lin2_out = self.linear2(relu_out)
+        list_out = self.list_output_layer([nn.Linear(2, 2)(lin2_out) for _ in range(2)])
+        resized_list_out = torch.cat(list_out, dim=1)
 
         lin3_out = self.linear3(lin1_out_alt)
         int_output = self.int_layer(lin3_out.to(torch.int64))
 
-        output_tensors = torch.cat((lin2_out, int_output), dim=1)
+        output_tensors = torch.cat((resized_list_out, int_output), dim=1)
 
         return (
             output_tensors