refactor to explicit registries

lm_eval/models/__init__.py

-from .gpt2 import GPT2LM
-from .gpt3 import GPT3LM
+from . import gpt2
+from . import gpt3
 
 MODEL_REGISTRY = {
-    "gpt2": GPT2LM,
-    "gpt3": GPT3LM,
+    "gpt2": gpt2.GPT2LM,
+    "gpt3": gpt3.GPT3LM,
 }
 
 

lm_eval/tasks/coqa.py

 import json
 import random
 from lm_eval.base import Dataset
-from . import TASK_REGISTRY
 
 
-@TASK_REGISTRY.register("coqa")
 class CoQA(Dataset):
     def has_training_docs(self):
         return True

lm_eval/tasks/superglue.py

 import numpy as np
 from tqdm import auto as tqdm_lib
 from . common import NLP_TASK, simple_accuracy_metric, yesno
-from . import TASK_REGISTRY
 
 
-@TASK_REGISTRY.register("boolq")
 class BoolQ(NLP_TASK):
     NLP_PATH = "super_glue"
     NLP_NAME = "boolq"
@@ -38,7 +36,6 @@ class BoolQ(NLP_TASK):
         return simple_accuracy_metric(preds=preds, golds=golds)
 
 
-@TASK_REGISTRY.register("cb")
 class CommitmentBank(NLP_TASK):
     NLP_PATH = "super_glue"
     NLP_NAME = "cb"
@@ -82,7 +79,6 @@ class CommitmentBank(NLP_TASK):
         return simple_accuracy_metric(preds=preds, golds=golds)
 
 
-@TASK_REGISTRY.register("copa")
 class Copa(NLP_TASK):
     NLP_PATH = "super_glue"
     NLP_NAME = "copa"
@@ -124,7 +120,6 @@ class Copa(NLP_TASK):
         return choice[0].lower() + choice[1:]
 
 
-@TASK_REGISTRY.register("wic")
 class WordsInContext(NLP_TASK):
     NLP_PATH = "super_glue"
     NLP_NAME = "wic"
@@ -162,7 +157,6 @@ class WordsInContext(NLP_TASK):
         return simple_accuracy_metric(preds=preds, golds=golds)
 
 
-@TASK_REGISTRY.register("wsc")
 class WinogradSchemaChallenge(NLP_TASK):
     NLP_PATH = "super_glue"
     NLP_NAME = "wsc"