Fix minor edge cases (#951 #1503) (#1520)

* Fix padding

* Fix elif in model loading

* format

lm_eval/evaluator.py

@@ -152,7 +152,7 @@ def simple_evaluate(
         if model_args is None:
             model_args = ""
 
-        elif isinstance(model_args, dict):
+        if isinstance(model_args, dict):
             lm = lm_eval.api.registry.get_model(model).create_from_arg_obj(
                 model_args,
                 {
@@ -348,10 +348,16 @@ def evaluate(
             gathered_item = (
                 lm.accelerator.gather(instances_rnk).cpu().detach().numpy().tolist()
             )
-
+            # "multiple_choice" task types dispatch (several) "loglikelihood" request types
+            reqtype = (
+                "loglikelihood"
+                if task.OUTPUT_TYPE == "multiple_choice"
+                else task.OUTPUT_TYPE
+            )
             # compute number of pseudo-batches to pad with (FSDP/DDP require even batches among ranks)
             numpad = max(gathered_item) - gathered_item[lm.rank]
-            padding_requests[task.OUTPUT_TYPE] += numpad
+            # todo: may not account for padding in cases like SquadV2 which has multiple req types
+            padding_requests[reqtype] += numpad
 
     ### Run LM on inputs, get all outputs ###
     # execute each type of request