Optimize VBE input generation (#1854)

Summary:

While authoring VBE benchmarks, this code block was very inefficient as determined by the profiler: https://www.internalfb.com/fburl?nopassthru=1&key=scuba%2Fpyperf_experimental%2Fon_demand%2Fnbkvd0xv. This diff optimizes the code by vectorizing the addition and appending to the list

Differential Revision: D55882021

torchrec/distributed/test_utils/test_model.py

@@ -304,6 +304,7 @@ def generate_variable_batch_input(
         strides_per_rank_per_feature = {}
         inverse_indices_per_rank_per_feature = {}
         label_per_rank = []
+
         for rank in range(world_size):
             # keys, values, lengths, strides
             lengths_per_rank_per_feature[rank] = {}
@@ -375,12 +376,11 @@ def generate_variable_batch_input(
             accum_batch_size = 0
             inverse_indices = []
             for rank in range(world_size):
-                inverse_indices += [
-                    index + accum_batch_size
-                    for index in inverse_indices_per_rank_per_feature[rank][key]
-                ]
+                inverse_indices.append(
+                    inverse_indices_per_rank_per_feature[rank][key] + accum_batch_size
+                )
                 accum_batch_size += strides_per_rank_per_feature[rank][key]
-            inverse_indices_list.append(torch.IntTensor(inverse_indices))
+            inverse_indices_list.append(torch.cat(inverse_indices))
         global_inverse_indices = (list(keys.keys()), torch.stack(inverse_indices_list))
         if global_constant_batch:
             global_offsets = []