Merge branch 'master' into task_doc

lm_eval/tasks/headqa.py

@@ -8,7 +8,8 @@ even for highly specialized humans.
 
 Homepage: https://aghie.github.io/head-qa/
 """
-from . common import HFTask
+import inspect
+import lm_eval.datasets.headqa.headqa
 from lm_eval.base import MultipleChoiceTask
 
 
@@ -24,9 +25,9 @@ _CITATION = """
 """
 
 
-class HeadQABase(HFTask, MultipleChoiceTask):
+class HeadQABase(MultipleChoiceTask):
     VERSION = 0
-    DATASET_PATH = "head_qa"
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.headqa.headqa)
 
     def has_training_docs(self):
         return True
@@ -37,7 +38,18 @@ class HeadQABase(HFTask, MultipleChoiceTask):
     def has_test_docs(self):
         return True
 
-    def _convert_standard(self, doc):
+    def training_docs(self):
+        if self._training_docs is None:
+            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
+        return self._training_docs
+
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
+
+    def test_docs(self):
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
         out_doc = {
             "id": doc["qid"],
             "query": "Question: " + doc["qtext"] + "\nAnswer:",
@@ -49,16 +61,25 @@ class HeadQABase(HFTask, MultipleChoiceTask):
     def doc_to_text(self, doc):
         return doc["query"]
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["query"]
+
+
 class HeadQAEn(HeadQABase):
     DATASET_NAME = "en"
 
+
 class HeadQAEs(HeadQABase):
     DATASET_NAME = "es"
 
+
 # for backwards compatibility
 class HeadQAEsDeprecated(HeadQABase):
     DATASET_NAME = "es"
 
     def __init__(self):
         super().__init__()
-        print("WARNING: headqa is deprecated. Please use headqa_es or headqa_en instead. See https://github.com/EleutherAI/lm-evaluation-harness/pull/240 for more info.")
+        print("WARNING: headqa is deprecated. Please use headqa_es or headqa_en instead. See https://github.com/EleutherAI/lm-evaluation-harness/pull/240 for more info.")
\ No newline at end of file

lm_eval/tasks/hellaswag.py

@@ -15,7 +15,6 @@ Homepage: https://rowanzellers.com/hellaswag/
 """
 import re
 from lm_eval.base import MultipleChoiceTask
-from . common import HFTask
 
 
 _CITATION = """
@@ -28,7 +27,7 @@ _CITATION = """
 """
 
 
-class HellaSwag(HFTask, MultipleChoiceTask):
+class HellaSwag(MultipleChoiceTask):
     VERSION = 0
     DATASET_PATH = "hellaswag"
     DATASET_NAME = None
@@ -42,16 +41,15 @@ class HellaSwag(HFTask, MultipleChoiceTask):
     def has_test_docs(self):
         return False
 
-    @classmethod
-    def preprocess(cls, text):
-        text = text.strip()
-        # NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
-        text = text.replace(" [title]", ". ")
-        text = re.sub('\\[.*?\\]', '', text)
-        text = text.replace("  ", " ")
-        return text
+    def training_docs(self):
+        if self._training_docs is None:
+            self._training_docs = list(map(self._process_doc, self.dataset["train"]))
+        return self._training_docs
+
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
 
-    def _convert_standard(self, doc):
+    def _process_doc(self, doc):
         ctx = doc["ctx_a"] + " " + doc["ctx_b"].capitalize()
         out_doc = {
             "query": self.preprocess(doc['activity_label'] + ': ' + ctx),
@@ -60,5 +58,20 @@ class HellaSwag(HFTask, MultipleChoiceTask):
         }
         return out_doc
 
+    @classmethod
+    def preprocess(cls, text):
+        text = text.strip()
+        # NOTE: Brackets are artifacts of the WikiHow dataset portion of HellaSwag.
+        text = text.replace(" [title]", ". ")
+        text = re.sub('\\[.*?\\]', '', text)
+        text = text.replace("  ", " ")
+        return text
+
     def doc_to_text(self, doc):
         return doc["query"]
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["query"]

lm_eval/tasks/hendrycks_math.py

@@ -8,13 +8,10 @@ models to generate answer derivations and explanations.
 
 Homepage: https://github.com/hendrycks/math
 """
-import abc
-import json
-from lm_eval.utils import sh
+import inspect
+import lm_eval.datasets.hendrycks_math.hendrycks_math
 from lm_eval.metrics import mean
 from lm_eval.base import Task, rf
-from pathlib import Path
-from best_download import download_file
 
 
 _CITATION = """
@@ -28,21 +25,8 @@ _CITATION = """
 
 
 class Math(Task):
-    DATASET_PATH = Path('data/MATH')
-
-    def download(self):
-        if not (self.DATASET_PATH / 'test').exists() or not (self.DATASET_PATH / 'done').exists():
-            sh(f"mkdir -p {self.DATASET_PATH}")
-            download_file("https://people.eecs.berkeley.edu/~hendrycks/MATH.tar", local_file=f"{self.DATASET_PATH}.tar", expected_checksum="0fbe4fad0df66942db6c221cdcc95b298cc7f4595a2f0f518360cce84e90d9ac")
-            sh(f"""
-            tar -xf {self.DATASET_PATH}.tar -C data/ && touch {self.DATASET_PATH / 'done'}
-            rm {self.DATASET_PATH}.tar
-            """)
-
-    @abc.abstractmethod
-    def get_file_info(self):
-        """returns directory name"""
-        pass
+    DATASET_PATH = inspect.getfile(lm_eval.datasets.hendrycks_math.hendrycks_math)
+    DATASET_NAME = None
 
     def has_training_docs(self):
         return True
@@ -53,28 +37,31 @@ class Math(Task):
     def has_test_docs(self):
         return True
 
-    def _load_docs(self, path):
-        for file in sorted(path.iterdir()):
-            with open(file) as f:
-                doc = json.load(f)
-                doc["answer"] = self.remove_boxed(
-                    self.last_boxed_only_string(doc["solution"]))
-                yield doc
-
     def training_docs(self):
-        return self._load_docs(self.DATASET_PATH / "train" / self.get_file_info())
+        return map(self._process_doc, self.dataset["train"])
 
     def validation_docs(self):
         return NotImplemented
 
     def test_docs(self):
-        return self._load_docs(self.DATASET_PATH / "test" / self.get_file_info())
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
+        doc["answer"] = self.remove_boxed(
+            self.last_boxed_only_string(doc["solution"]))
+        return doc
 
     def doc_to_text(self, doc):
         return "Problem: " + doc["problem"] + "\nAnswer:"
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["problem"]
+
     def doc_to_target(self, doc):
-        return " " + doc["answer"]
+        return " " + doc["solution"]
 
     def construct_requests(self, doc, ctx):
         return rf.greedy_until(ctx, ["\n"])
@@ -301,41 +288,34 @@ class Math(Task):
 
 class MathAlgebra(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'algebra'
+    DATASET_NAME = 'algebra'
 
 
 class MathCountingAndProbability(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'counting_and_probability'
+    DATASET_NAME = 'counting_and_probability'
 
 
 class MathGeometry(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'geometry'
+    DATASET_NAME = 'geometry'
 
 
 class MathIntermediateAlgebra(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'intermediate_algebra'
+    DATASET_NAME = 'intermediate_algebra'
 
 
 class MathNumberTheory(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'number_theory'
+    DATASET_NAME = 'number_theory'
 
 
 class MathPrealgebra(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'prealgebra'
+    DATASET_NAME = 'prealgebra'
 
 
 class MathPrecalculus(Math):
     VERSION = 1
-    def get_file_info(self):
-        return 'precalculus'
+    DATASET_NAME = 'precalculus'

lm_eval/tasks/hendrycks_test.py

@@ -12,12 +12,7 @@ important shortcomings.
 
 Homepage: https://github.com/hendrycks/test
 """
-import csv
-import random
 from lm_eval.base import MultipleChoiceTask
-from ..utils import sh
-from pathlib import Path
-from best_download import download_file
 
 
 _CITATION = """
@@ -61,25 +56,15 @@ def create_task(subject):
 
 class GeneralHendrycksTest(MultipleChoiceTask):
     VERSION = 0
-    DATASET_PATH = Path("data/hendrycksTest/")
+    DATASET_PATH = "hendrycks_test"
+    DATASET_NAME = None
 
     def __init__(self, subject):
-        self.subject = subject
+        self.DATASET_NAME = subject
         super().__init__()
 
-    def download(self):
-        if not (self.DATASET_PATH / 'done').exists():
-            sh("mkdir -p data")
-            download_file("https://people.eecs.berkeley.edu/~hendrycks/data.tar", local_file="data/data.tar", expected_checksum="78a804365a59028188fb19bd1adcadc5e0c260b220a9d8b2e33a5ea7d5fbe3b4")
-            sh("""
-            tar -xf data/data.tar -C data/
-            rm data/data.tar
-            mv data/data data/hendrycksTest
-            touch data/hendrycksTest/done
-            """)
-
     def has_training_docs(self):
-        return True
+        return False
 
     def has_validation_docs(self):
         return True
@@ -87,8 +72,14 @@ class GeneralHendrycksTest(MultipleChoiceTask):
     def has_test_docs(self):
         return True
 
-    def _convert_standard(self, doc):
-        def format_example(doc, choices):
+    def validation_docs(self):
+        return map(self._process_doc, self.dataset["validation"])
+
+    def test_docs(self):
+        return map(self._process_doc, self.dataset["test"])
+
+    def _process_doc(self, doc):
+        def format_example(doc, keys):
             """
                 Question: <prompt>
                 Choices:
@@ -98,46 +89,31 @@ class GeneralHendrycksTest(MultipleChoiceTask):
                 D. <choice4>
                 Answer:
             """
-            prompt = "Question: " + doc[0] + "\nChoices:\n"
-            prompt += "".join([f"{choices[j]}. {doc[j+1]}\n" for j in range(4)])
+            prompt = "Question: " + doc["question"] + "\nChoices:\n"
+            prompt += "".join([f"{key}. {choice}\n" for key, choice in zip(keys, doc["choices"])])
             prompt += "Answer:"
             return prompt
-        choices = ['A', 'B', 'C', 'D']
+        keys = ['A', 'B', 'C', 'D']
         return {
-            "query": format_example(doc, choices),
-            "choices": doc[1:5],
-            "gold": choices.index(doc[5])
+            "query": format_example(doc, keys),
+            "choices": doc["choices"],
+            "gold": keys.index(doc["answer"]) if isinstance(doc["answer"], str) else doc["answer"]
         }
 
-    def _load_docs(self, filename):
-        reader = csv.reader(open(filename, 'r'), quotechar='"', delimiter=',')
-        return (self._convert_standard(doc) for doc in reader)
-
-    def training_docs(self):
-        docs = []
-        for train_dir in ["auxiliary_train", "dev"]:
-            for f in (self.DATASET_PATH / train_dir).iterdir():
-                docs.extend(self._load_docs(f))
-        return docs
-
-    def validation_docs(self):
-        filename = self.DATASET_PATH / "val" / f"{self.subject}_val.csv"
-        return self._load_docs(filename)
-
-    def test_docs(self):
-        filename = self.DATASET_PATH / "test" / f"{self.subject}_test.csv"
-        return self._load_docs(filename)
-
     def fewshot_examples(self, k, rnd):
         # fewshot_examples is not just sampling from train_docs because dev is 
         # in the same distribution as val/test but auxiliary_train isn't
 
-        filename = self.DATASET_PATH / "dev" / f"{self.subject}_dev.csv"
-
         if self._fewshot_docs is None:
-            self._fewshot_docs = list(self._load_docs(filename))
+            self._fewshot_docs = list(map(self._process_doc, self.dataset["dev"]))
 
         return rnd.sample(list(self._fewshot_docs), k)
 
     def doc_to_text(self, doc):
         return doc["query"]
+
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc["query"]

lm_eval/tasks/lambada_cloze.py

@@ -13,12 +13,7 @@ in the broader discourse.
 
 Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
 """
-import json
-from lm_eval.base import Task, rf
-from lm_eval.metrics import mean, perplexity
-from lm_eval.utils import sh
 from lm_eval.tasks.lambada import LAMBADA
-from best_download import download_file
 
 
 _CITATION = """
@@ -35,8 +30,15 @@ _CITATION = """
 
 class LAMBADA_cloze(LAMBADA):
     VERSION = 0
+
     def doc_to_text(self, doc):
         return doc['text'].rsplit(' ', 1)[0] + " ____. ->"
 
+    def should_decontaminate(self):
+        return True
+
+    def doc_to_decontamination_query(self, doc):
+        return doc['text']
+
     def doc_to_target(self, doc):
         return " " + doc['text'].rsplit(' ', 1)[1]