Merge branch 'master' into race-evaluation

lm_eval/base.py

@@ -59,7 +59,6 @@ class LM(abc.ABC):
 
 
 class Dataset(abc.ABC):
-    @abc.abstractmethod
     def __init__(self):
         self.download()
         self._traindocs = None

lm_eval/tasks/__init__.py

@@ -15,6 +15,8 @@ from . import sat
 from . import arithmetic
 from . import lambada
 from . import race 
+from . import piqa
+
 
 TASK_REGISTRY = {
     # GLUE
@@ -40,11 +42,12 @@ TASK_REGISTRY = {
     # Order by benchmark/genre?
 
     "lambada": lambada.LAMBADA,
+    "piqa": piqa.PiQA,
 
     # "arc_easy": arc.ARCEasy, # not implemented yet
     # "arc_challenge": arc.ARCChallenge, # not implemented yet
     # "quac": quac.QuAC, # not implemented yet
-    # "hellaswag": hellaswag.HellaSwag, # not implemented yet
+    "hellaswag": hellaswag.HellaSwag, # not implemented yet
     # "openbookqa": openbookqa.OpenBookQA, # not implemented yet
     # "sat": sat.SATAnalogies, # not implemented yet
     # "squad": squad.SQuAD, # not implemented yet

lm_eval/tasks/hellaswag.py

+import re
 import numpy as np
-from scipy.stats import pearsonr, spearmanr
-from sklearn.metrics import f1_score, matthews_corrcoef
-from tqdm import auto as tqdm_lib
-from . common import HFTask, simple_accuracy_metric, yesno
+from ..base import rf, mean
+from . common import HFTask
+
 
 class HellaSwag(HFTask):
     DATASET_PATH = "hellaswag"
     DATASET_NAME = None
 
+    @classmethod
+    def remove_brackets(cls, text):
+        """ Removes brackets from HellaSwag documents. 
+        NOTE: The brackets are artifacts of the WikiHow dataset portion underlying
+        HellaSwag.
+        """
+        text = re.sub('\[.*?\]', '', text)
+        return text
+
     def has_training_docs(self):
         return True
 
@@ -30,10 +39,13 @@ class HellaSwag(HFTask):
             return self.data["test"]
 
     def fewshot_description(self):
-        return "Label for the relevant action: Sentences describing the context, with an incomplete sentence trailing\nanswer that plausibly completes the situation."
+        return "Label for the relevant action: Sentences describing the " \
+            "context, with an incomplete sentence trailing\nanswer that " \
+            "plausibly completes the situation."
 
     def doc_to_text(self, doc):
-        return doc['activity_label'] + ': ' + doc['ctx'] + '\n'
+        text = doc['activity_label'] + ': ' + doc['ctx'] + '\n'
+        return self.remove_brackets(text)
 
     def doc_to_target(self, doc):
         letter_answer = doc['label']
@@ -46,50 +58,59 @@ class HellaSwag(HFTask):
         elif letter_answer == '3':
             index = 3
         else:
-            raise ValueError("HellaSwag from HF datasets contained an invalid answer key")
-        return doc['endings'][index]
+            raise ValueError(
+                "HellaSwag from HF datasets contained an invalid answer key")
+        target = doc['endings'][index]
+        return self.remove_brackets(target)
 
     def construct_requests(self, doc, ctx):
-        """ Uses RequestFactory to construct Requests and returns an iterable of 
+        """ Uses RequestFactory to construct Requests and returns an iterable of
         Requests which will be sent to the LM.
-
         :param doc:
             The document as returned from training_docs, validation_docs, or test_docs.
         :param ctx: str
-            The context string, generated by fewshot_context. This includes the natural 
+            The context string, generated by fewshot_context. This includes the natural
             language description, as well as the few shot examples, and the question
-            part of the document for `doc`. 
+            part of the document for `doc`.
         """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
-    
+        ll_answers = []
+        for i in range(4):
+            continuation = self.remove_brackets(doc['endings'][i])
+            ll_answers.append(rf.loglikelihood(ctx, continuation))
+        return ll_answers
+
     def process_results(self, doc, results):
-        """Take a single document and the LM results and evaluates, returning a 
-        dict where keys are the names of submetrics and values are the values of 
+        """Take a single document and the LM results and evaluates, returning a
+        dict where keys are the names of submetrics and values are the values of
         the metric for that one document
-
         :param doc:
             The document as returned from training_docs, validation_docs, or test_docs.
         :param results:
             The results of the requests created in construct_requests.
         """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        gold = int(doc['label'])
+        pred = np.argmax(results)
+        acc = 1. if pred == gold else 0.
+        return {
+            "acc": acc
+        }
 
     def aggregation(self):
         """
         :returns: {str: [float] -> float}
-            A dictionary where keys are the names of submetrics and values are 
+            A dictionary where keys are the names of submetrics and values are
             functions that aggregate a list of metrics
         """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        return {
+            "acc": mean
+        }
 
     def higher_is_better(self):
         """
         :returns: {str: bool}
-            A dictionary where keys are the names of submetrics and values are 
+            A dictionary where keys are the names of submetrics and values are
             whether a higher value of the submetric is better
         """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        return {
+            "acc": True
\ No newline at end of file
+        }

lm_eval/tasks/piqa.py

 import json
 import random
-from lm_eval.base import Dataset
+from lm_eval.base import Dataset, rf, mean
 from ..utils import sh
+import os
 
 class PiQA(Dataset):
-    def __init__(self):
-        self.download()
     def download(self):
-        #pass
-        #TODO: don't download if files already there
-        sh("""
-           mkdir -p data/piqa
-           wget https://yonatanbisk.com/piqa/data/train.jsonl -O data/piqa/piqa-train.jsonl
-           wget https://yonatanbisk.com/piqa/data/train-labels.lst -O data/piqa/piqa-train-labels.lst
-           wget https://yonatanbisk.com/piqa/data/valid.jsonl -O data/piqa/piqa-valid.jsonl
-           wget https://yonatanbisk.com/piqa/data/valid-labels.lst -O data/piqa/piqa-valid-labels.lst
-           wget https://yonatanbisk.com/piqa/data/tests.jsonl -O data/piqa/piqa-test.jsonl
-           """)
+        if not os.path.exists('data/piqa'):
+            #TODO: use best_download
+            sh("""
+            mkdir -p data/piqa
+            wget https://yonatanbisk.com/piqa/data/train.jsonl -O data/piqa/piqa-train.jsonl
+            wget https://yonatanbisk.com/piqa/data/train-labels.lst -O data/piqa/piqa-train-labels.lst
+            wget https://yonatanbisk.com/piqa/data/valid.jsonl -O data/piqa/piqa-valid.jsonl
+            wget https://yonatanbisk.com/piqa/data/valid-labels.lst -O data/piqa/piqa-valid-labels.lst
+            wget https://yonatanbisk.com/piqa/data/tests.jsonl -O data/piqa/piqa-test.jsonl
+            """)
 
     def has_training_docs(self):
         return True
@@ -25,11 +24,11 @@ class PiQA(Dataset):
         return True
 
     def has_test_docs(self):
-        return True
+        return False
 
     def load_docs(self, textfilename, labelfilename):
         if labelfilename != None:
-            return zip([json.loads(entry) for entry in list(open(textfilename,'r'))],list(open(labelfilename, 'r')))
+            return zip([json.loads(entry) for entry in list(open(textfilename,'r'))],list(map(lambda x: x.strip(), open(labelfilename, 'r'))))
         else:
             return [json.loads(entry) for entry in list(open(textfilename,'r'))]
     
@@ -39,62 +38,40 @@ class PiQA(Dataset):
     def validation_docs(self):
         return self.load_docs('data/piqa/piqa-valid.jsonl', 'data/piqa/piqa-valid-labels.lst')
 
-    def test_docs(self):
-        return self.load_docs('data/piqa/piqa-test.jsonl', None)
+    #def test_docs(self):
+    #    return self.load_docs('data/piqa/piqa-test.jsonl', None)
     
     def fewshot_description(self):
         # TODO: figure out fewshot description
         return ""
     
     def doc_to_text(self, doc):
-        #TODO: check if oa uses newline
-        return  doc['goal'] + ' '
+        return doc[0]['goal']
 
     def doc_to_target(self, doc):
-        rightanswer = int(doc[1][0]) + 1
-        return ''.join([doc[0]['goal'],' ',doc[0]['sol'+str(rightanswer)]])
+        #TODO: check if oa uses newline
+        rightanswer = int(doc[1]) + 1
+        return '\n' + ''.join([doc[0]['goal'],' ',doc[0]['sol'+str(rightanswer)]])
 
     def construct_requests(self, doc, ctx):
-        """ Uses RequestFactory to construct Requests and returns an iterable of 
-        Requests which will be sent to the LM.
+        ll_1, _ = rf.loglikelihood(ctx, doc[0]['sol1'])
+        ll_2, _ = rf.loglikelihood(ctx, doc[0]['sol2'])
 
-        :param doc:
-            The document as returned from training_docs, validation_docs, or test_docs.
-        :param ctx: str
-            The context string, generated by fewshot_context. This includes the natural 
-            language description, as well as the few shot examples, and the question
-            part of the document for `doc`. 
-        """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        return ll_1, ll_2
     
     def process_results(self, doc, results):
-        """Take a single document and the LM results and evaluates, returning a 
-        dict where keys are the names of submetrics and values are the values of 
-        the metric for that one document
+        ll_1, ll_2 = results
 
-        :param doc:
-            The document as returned from training_docs, validation_docs, or test_docs.
-        :param results:
-            The results of the requests created in construct_requests.
-        """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        return {
+            'acc': (ll_1 > ll_2) == (int(doc[1]) == 0)
+        }
 
     def aggregation(self):
-        """
-        :returns: {str: [float] -> float}
-            A dictionary where keys are the names of submetrics and values are 
-            functions that aggregate a list of metrics
-        """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
+        return {
+            'acc': mean
+        }
 
     def higher_is_better(self):
-        """
-        :returns: {str: bool}
-            A dictionary where keys are the names of submetrics and values are 
-            whether a higher value of the submetric is better
+        return {
+            'acc': True
+        }
\ No newline at end of file
-        """
-        # TODO: implement evaluation.
-        raise NotImplementedError('Evaluation not implemented')
\ No newline at end of file