Revert batching for input reduction (#3276)

allennlp/interpret/attackers/input_reduction.py

@@ -80,31 +80,12 @@ def get_length(input_instance: Instance):
                 return len(input_text_field.tokens)
             candidates = heapq.nsmallest(self.beam_size, candidates, key=lambda x: get_length(x[0]))
 
-            # predictor.get_gradients is where the most expensive computation happens, so we're
-            # going to do it in a batch, up front, before iterating over the results.
-            copied_candidates = deepcopy(candidates)
-            all_grads, all_outputs = self.predictor.get_gradients([x[0] for x in copied_candidates])
-
-            # The output in `all_grads` and `all_outputs` is batched in a dictionary (e.g.,
-            # {'grad_output_1': batched_tensor}).  We need to split this into a list of non-batched
-            # dictionaries that we can iterate over.
-            split_grads = []
-            for i in range(len(copied_candidates)):
-                split_grads.append({key: value[i] for key, value in all_grads.items()})
-            split_outputs = []
-            for i in range(len(copied_candidates)):
-                instance_outputs = {}
-                for key, value in all_outputs.items():
-                    if key == 'loss':
-                        continue
-                    instance_outputs[key] = value[i]
-                split_outputs.append(instance_outputs)
-            beam_candidates = [(x[0], x[1], x[2], split_grads[i], split_outputs[i])
-                               for i, x in enumerate(copied_candidates)]
-
+            beam_candidates = deepcopy(candidates)
             candidates = []
-            for beam_instance, smallest_idx, tag_mask, grads, outputs in beam_candidates:
+            for beam_instance, smallest_idx, tag_mask in beam_candidates:
+                # get gradients and predictions
                 beam_tag_mask = deepcopy(tag_mask)
+                grads, outputs = self.predictor.get_gradients([beam_instance])
 
                 for output in outputs:
                     if isinstance(outputs[output], torch.Tensor):
@@ -133,7 +114,7 @@ def get_length(input_instance: Instance):
                 current_tokens = deepcopy(text_field.tokens)
                 reduced_instances_and_smallest = _remove_one_token(beam_instance,
                                                                    input_field_to_attack,
-                                                                   grads[grad_input_field],
+                                                                   grads[grad_input_field][0],
                                                                    ignore_tokens,
                                                                    self.beam_size,
                                                                    beam_tag_mask)