Merge branch 'researcher2' of https://github.com/EleutherAI/lm-evaluation-harness into researcher2

lm_eval/evaluator.py

 import collections
 import itertools
+import pathlib
 import random
 import lm_eval.metrics
 import lm_eval.models
@@ -7,14 +8,16 @@ import lm_eval.tasks
 import lm_eval.base
 import lm_eval.decontamination
 import numpy as np
-from lm_eval.utils import positional_deprecated
+from lm_eval.utils import positional_deprecated, run_task_tests
 from lm_eval.decontamination.decontaminate import get_train_overlap
 
 @positional_deprecated
 def simple_evaluate(model, model_args=None, tasks=[],
                     num_fewshot=0, batch_size=None, device=None,
                     no_cache=False, limit=None, bootstrap_iters=100000,
-                    description_dict=None, decontamination_ngrams_path=None):
+                    description_dict=None, check_integrity=False, 
+                    decontamination_ngrams_path=None):
+
     """Instantiate and evaluate a model on a list of tasks.
 
     :param model: Union[str, LM]
@@ -38,6 +41,8 @@ def simple_evaluate(model, model_args=None, tasks=[],
         Number of iterations for bootstrap statistics
     :param description_dict: dict[str, str]
         Dictionary of custom task descriptions of the form: `task_name: description` 
+    :param check_integrity: bool
+        Whether to run the relevant part of the test suite for the tasks
     :return
         Dictionary of results
     """
@@ -62,6 +67,9 @@ def simple_evaluate(model, model_args=None, tasks=[],
     
     task_dict = lm_eval.tasks.get_task_dict(tasks)
 
+    if check_integrity:
+        run_task_tests(task_list=tasks)
+
     results = evaluate(
         lm=lm,
         task_dict=task_dict,

lm_eval/models/dummy.py

@@ -7,7 +7,7 @@ class DummyLM(LM):
         pass
 
     @classmethod
-    def create_from_arg_string(cls, arg_string):
+    def create_from_arg_string(cls, arg_string, additional_config=None):
         return cls()
 
     def loglikelihood(self, requests):
@@ -23,7 +23,7 @@ class DummyLM(LM):
 
         for ctx, _ in requests:
             res.append("lol")
-            assert ctx.strip() != ''
+            assert ctx.strip() != ""
 
         return res
 

lm_eval/models/gpt3.py

@@ -46,6 +46,8 @@ def oa_completion(**kwargs):
         try:
             return openai.Completion.create(**kwargs)
         except openai.error.OpenAIError:
+            import traceback
+            traceback.print_exc()
             time.sleep(backoff_time)
             backoff_time *= 1.5
 

lm_eval/tasks/__init__.py

@@ -29,6 +29,7 @@ from . import triviaqa
 from . import pubmedqa
 from . import sciq
 from . import webqs
+from . import qasper
 from . import qa4mre
 from . import translation
 from . import headqa
@@ -48,6 +49,7 @@ from . import mutual
 from . import truthfulqa
 from . import blimp
 from . import asdiv
+from . import gsm8k
 
 ########################################
 # Translation tasks
@@ -121,6 +123,8 @@ TASK_REGISTRY = {
     "pubmedqa" : pubmedqa.Pubmed_QA,
     "sciq" : sciq.SciQ,
 
+    "qasper": qasper.QASPER,
+
     "qa4mre_2011" : qa4mre.QA4MRE_2011,
     "qa4mre_2012" : qa4mre.QA4MRE_2012,
     "qa4mre_2013" : qa4mre.QA4MRE_2013,
@@ -170,6 +174,7 @@ TASK_REGISTRY = {
     "math_prealgebra": hendrycks_math.MathPrealgebra,
     "math_precalc": hendrycks_math.MathPrecalculus,
     "math_asdiv": asdiv.Asdiv,
+    "gsm8k": gsm8k.GradeSchoolMath8K,
 
     # arithmetic
     "arithmetic_2da": arithmetic.Arithmetic2DPlus,

lm_eval/tasks/gsm8k.py

+"""
+"Training Verifiers to Solve Math Word Problems"
+https://arxiv.org/abs/2110.14168
+
+@misc{cobbe2021training,
+      title={Training Verifiers to Solve Math Word Problems},
+      author={Karl Cobbe and Vineet Kosaraju and Mohammad Bavarian and Jacob Hilton and Reiichiro Nakano and Christopher Hesse and John Schulman},
+      year={2021},
+      eprint={2110.14168},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+
+NOTE: See the official implementation of the task: 
+    https://github.com/openai/grade-school-math/blob/master/grade_school_math/calculator.py
+for how to make use of the dataset's calculator annotations in your language
+model's sample/generation function.
+"""
+
+import json
+import re
+from best_download import download_file
+from pathlib import Path
+from lm_eval.base import Task, rf
+from lm_eval.metrics import mean
+
+ANS_RE = re.compile(r"#### (\-?[0-9\.\,]+)")
+INVALID_ANS = "[invalid]"
+
+
+class GradeSchoolMath8K(Task):
+    VERSION = 0
+    DATASET_PATH = Path('data/gsm8k')
+
+    def download(self):
+        if self.DATASET_PATH.exists():
+            return
+        Path.mkdir(self.DATASET_PATH, parents=True)
+        base_url = "https://raw.githubusercontent.com/openai/grade-school-math/master/grade_school_math/data"
+        splits = [
+            {"name": "train", "checksum": "17f347dc51477c50d4efb83959dbb7c56297aba886e5544ee2aaed3024813465"},
+            {"name": "test", "checksum": "3730d312f6e3440559ace48831e51066acaca737f6eabec99bccb9e4b3c39d14"},
+        ]
+        for split in splits:
+            file = self.DATASET_PATH / f"{split['name']}.jsonl"
+            download_file(f"{base_url}/{split['name']}.jsonl", str(file), split["checksum"])
+
+    def has_training_docs(self):
+        return True
+
+    def has_validation_docs(self):
+        return False
+
+    def has_test_docs(self):
+        return True
+
+    def _load_docs(self, file):
+        return (json.loads(line) for line in open(file).read().splitlines())
+
+    def training_docs(self):
+        return self._load_docs(self.DATASET_PATH / "train.jsonl")
+
+    def validation_docs(self):
+        raise NotImplementedError
+
+    def test_docs(self):
+        return self._load_docs(self.DATASET_PATH / "test.jsonl")
+
+    def doc_to_text(self, doc):
+        return "Question: " + doc['question'] + '\nAnswer:'
+
+    def doc_to_target(self, doc):
+        return " " + doc['answer']
+
+    def construct_requests(self, doc, ctx):
+        """ 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
+            language description, as well as the few shot examples, and the question
+            part of the document for `doc`.
+        """
+        # NOTE: The paper implements "verifiers" that assign a score to multiple 
+        # solutions and output the highest ranked solution.
+        completion = rf.greedy_until(ctx, ['\n'])
+        return completion 
+
+    def _extract_answer(self, completion):
+        match = ANS_RE.search(completion)
+        if match:
+            match_str = match.group(1).strip()
+            match_str = match_str.replace(",", "")
+            return match_str
+        else:
+            return INVALID_ANS
+
+    def _is_correct(self, completion, answer):
+        gold = self._extract_answer(answer)
+        assert gold != INVALID_ANS, "No ground truth answer found in the document."
+        return self._extract_answer(completion) == gold 
+
+    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
+
+        :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.
+        """
+        completion = results[0]
+        answer = doc["answer"]
+        return {
+            "acc": self._is_correct(completion, answer)
+        }
+
+    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
+        """
+        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
+        }

lm_eval/tasks/hendrycks_math.py

@@ -18,7 +18,7 @@ class Math(Task):
     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="01256fd7cd5430596fdf07e6e6a5827111b5235b7ffed679c662a12f898932da")
+            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
@@ -291,42 +291,42 @@ class Math(Task):
 
 
 class MathAlgebra(Math):
-    VERSION = 0
+    VERSION = 1
     def get_file_info(self):
         return 'algebra'
 
 
 class MathCountingAndProbability(Math):
-    VERSION = 0
+    VERSION = 1
     def get_file_info(self):
         return 'counting_and_probability'
 
 
 class MathGeometry(Math):
-    VERSION = 0
+    VERSION = 1
     def get_file_info(self):
         return 'geometry'
 
 
 class MathIntermediateAlgebra(Math):
-    VERSION = 0
+    VERSION = 1
     def get_file_info(self):
         return 'intermediate_algebra'
 
 
 class MathNumberTheory(Math):
-    VERSION = 0
+    VERSION = 1
     def get_file_info(self):
         return 'number_theory'
 
 
 class MathPrealgebra(Math):
-    VERSION = 0
+    VERSION = 1
     def get_file_info(self):
         return 'prealgebra'
 
 
 class MathPrecalculus(Math):
-    VERSION = 0
+    VERSION = 1
     def get_file_info(self):
         return 'precalculus'

lm_eval/tasks/qasper.py

+""" 
+A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
+https://arxiv.org/abs/2105.03011
+
+@article{DBLP:journals/corr/abs-2105-03011,
+  author    = {Pradeep Dasigi and
+               Kyle Lo and
+               Iz Beltagy and
+               Arman Cohan and
+               Noah A. Smith and
+               Matt Gardner},
+  title     = {A Dataset of Information-Seeking Questions and Answers Anchored in
+               Research Papers},
+  journal   = {CoRR},
+  volume    = {abs/2105.03011},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2105.03011},
+  eprinttype = {arXiv},
+  eprint    = {2105.03011},
+  timestamp = {Fri, 14 May 2021 12:13:30 +0200},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2105-03011.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+from collections import Counter
+from math import exp
+import random
+import re
+import string
+from lm_eval.base import rf
+from lm_eval.metrics import f1_score, mean
+from .common import HFTask
+
+
+def normalize_answer(s):
+    """
+    Taken from the official evaluation script for v1.1 of the SQuAD dataset.
+    Lower text and remove punctuation, articles and extra whitespace.
+    """
+
+    def remove_articles(text):
+        return re.sub(r"\b(a|an|the)\b", " ", text)
+
+    def white_space_fix(text):
+        return " ".join(text.split())
+
+    def remove_punc(text):
+        exclude = set(string.punctuation)
+        return "".join(ch for ch in text if ch not in exclude)
+
+    def lower(text):
+        return text.lower()
+
+    return white_space_fix(remove_articles(remove_punc(lower(s))))
+
+
+def categorise_answer(answer_blob):
+    if answer_blob["unanswerable"]:
+        answer = "unanswerable"
+        answer_type = "unanswerable"
+        return answer, answer_type
+    elif answer_blob["yes_no"]:
+        answer = "yes"
+        answer_type = "bool"
+        return answer, answer_type
+    elif answer_blob["free_form_answer"]:
+        answer = answer_blob["free_form_answer"]
+        answer_type = "free form answer"
+        return answer, answer_type
+    elif answer_blob["extractive_spans"]:
+        answer = answer_blob["extractive_spans"]
+        answer_type = "extractive_spans"
+        return answer, answer_type
+    elif answer_blob["yes_no"] is False:
+        answer = "no"
+        answer_type = "bool"
+        return answer, answer_type
+
+
+def token_f1_score(prediction, ground_truth):
+    """
+    Taken from the official evaluation script for v1.1 of the SQuAD dataset.
+    """
+    prediction_tokens = normalize_answer(prediction).split()
+    ground_truth_tokens = normalize_answer(ground_truth).split()
+    common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
+    num_same = sum(common.values())
+    if num_same == 0:
+        return 0
+    precision = 1.0 * num_same / len(prediction_tokens)
+    recall = 1.0 * num_same / len(ground_truth_tokens)
+    f1 = (2 * precision * recall) / (precision + recall)
+    return f1
+
+
+class QASPER(HFTask):
+    VERSION = 0
+    DATASET_PATH = "qasper"
+    DATASET_NAME = None
+
+    def doc_to_text(self, doc):
+        return (
+            "TITLE: "
+            + doc["title"]
+            + "\n"
+            + "ABSTRACT: "
+            + doc["abstract"]
+            + "\n\n"
+            + "Q: "
+            + doc["question"]
+            + "\n\n"
+            + "A:"
+        )
+
+    def doc_to_target(self, doc):
+        answer = doc["answer"]
+        if isinstance(answer, list):
+            answer = ", ".join(answer)
+        return " " + answer
+
+    def training_docs(self):
+        for doc in self.data["train"]:
+            yield from self.process_doc(doc)
+
+    def validation_docs(self):
+        for doc in self.data["train"]:
+            yield from self.process_doc(doc)
+
+    def process_doc(self, doc):
+        """Given a `doc`, flatten it out so that each JSON blob
+        contains exactly one question and one answer. Logic taken from
+        the reference implementation available at
+        https://github.com/allenai/qasper-led-baseline/blob/main/scripts/evaluator.py
+        """
+        obs_list = []
+        for question, answer_list in zip(doc["qas"]["question"], doc["qas"]["answers"]):
+            for answer_blob in answer_list["answer"]:
+                answer, answer_type = categorise_answer(answer_blob)
+                obs_list.append(
+                    {
+                        "title": doc["title"],
+                        "abstract": doc["abstract"],
+                        "question": question,
+                        "answer": answer,
+                        "answer_type": answer_type,
+                    }
+                )
+        return obs_list
+
+    def process_results(self, doc, results):
+        # TODO: Calculate a score for extractive spans once a request type for generating
+        # extractive spans is available
+        if not results:
+            return {}
+        elif len(results) == 1:
+            [res] = results
+        elif len(results) == 2:
+            [ll_yes, ll_no] = results
+
+        # TODO: Handle unanswerability first
+        # unanswerable_gold = doc["answer_type"] == "unanswerable"
+        # unanswerable_pred = exp(logprob_unanswerable)
+        # res_dict["f1_unanswerable"] = (unanswerable_gold, unanswerable_pred)
+
+        res_dict = {}
+        # Handle yes/no questions
+        if doc["answer_type"] == "bool":
+            gold = 1 if doc["answer"] == "yes" else 0
+            pred = ll_yes > ll_no
+            res_dict["f1_yesno"] = (gold, pred)
+
+        # Handle completions
+        if doc["answer_type"] == "free form answer":
+            res_dict["f1_abstractive"] = token_f1_score(res, doc["answer"])
+
+        # TODO: Handle extraction
+        # if doc["answer_type"] == "extractive_spans":
+        #     res_dict["f1_extractive"] = 0
+        return res_dict
+
+    def aggregation(self):
+        return {
+            "f1_yesno": f1_score,
+            "f1_abstractive": mean,
+        }
+
+    def construct_requests(self, doc, ctx):
+        """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
+            language description, as well as the few shot examples, and the question
+            part of the document for `doc`.
+        """
+        # unanswerable = rf.loglikelihood(ctx, " " + "unanswerable")
+        if doc["answer_type"] in ("free form answer"):
+            return [rf.greedy_until(ctx, ["\n"])]
+        elif doc["answer_type"] in ("bool"):
+            ll_yes, _ = rf.loglikelihood(ctx, " yes")
+            ll_no, _ = rf.loglikelihood(ctx, " no")
+            return [ll_yes, ll_no]
+        else:
+            return []
+
+    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 {
+            "f1_yesno": True,
+            "f1_abstractive": True,
+        }

lm_eval/utils.py

 import os
+import pathlib
 import re
 import collections
 import functools
 import inspect
+import sys
+import pytest
+from typing import List
 
 
 class ExitCodeError(Exception):
@@ -155,3 +159,32 @@ def positional_deprecated(fn):
                 "lm-evaluation-harness!")
         return fn(*args, **kwargs)
     return _wrapper
+
+@positional_deprecated
+def find_test_root(start_path: pathlib.Path) -> pathlib.Path:
+    """
+    Search upward in the directory tree to a maximum of three layers
+    to find and return the package root (containing the 'tests' folder)
+    """
+    cur_path = start_path.resolve()
+    max_layers = 3
+    for _ in range(max_layers):
+        if (cur_path / 'tests' / 'test_version_stable.py').exists():
+            return cur_path
+        else:
+            cur_path = cur_path.parent.resolve()
+    raise FileNotFoundError(f"Unable to find package root within {max_layers} upwards" +\
+        f"of {start_path}")
+
+@positional_deprecated
+def run_task_tests(task_list: List[str]):
+    """
+    Find the package root and run the tests for the given tasks
+    """
+    package_root = find_test_root(start_path=pathlib.Path(__file__))
+    task_string = ' or '.join(task_list)
+    args = [f'{package_root}/tests/test_version_stable.py', f'--rootdir={package_root}', '-k', f'{task_string}']
+    sys.path.append(str(package_root))
+    pytest_return_val = pytest.main(args)
+    if pytest_return_val:
+        raise ValueError(f"Not all tests for the specified tasks ({task_list}) ran successfully! Error code: {pytest_return_val}")
\ No newline at end of file

main.py

@@ -37,6 +37,7 @@ def parse_args():
     parser.add_argument('--no_cache', action="store_true")
     parser.add_argument('--decontamination_ngrams_path', default=None)
     parser.add_argument('--description_dict_path', default=None)  
+    parser.add_argument('--check_integrity', action="store_true")  
 
     return parser.parse_args()
 
@@ -79,7 +80,8 @@ def main():
         no_cache=args.no_cache,
         limit=args.limit,
         description_dict=description_dict,
-        decontamination_ngrams_path=args.decontamination_ngrams_path
+        decontamination_ngrams_path=args.decontamination_ngrams_path,
+        check_integrity=args.check_integrity
     )
 
     dumped = json.dumps(results, indent=2)    

setup.py

@@ -21,7 +21,7 @@ setuptools.setup(
     python_requires='>=3.6',
     install_requires=[
         "black",
-        "best_download>=0.0.6",
+        "best_download==0.0.9",
         "datasets==1.15.1",
         "click>=7.1",
         "scikit-learn>=0.24.1",

tests/testdata/gsm8k-v0-greedy_until

+e7292dbdd7fd8419ba954f2e0701e04c8d0e8842fe053dbf2fe47d926630e35e
\ No newline at end of file

tests/testdata/gsm8k-v0-res.json

+{"results": {"gsm8k": {"acc": 0.0, "acc_stderr": 0.0}}, "versions": {"gsm8k": 0}}
\ No newline at end of file

tests/testdata/math_algebra-v1-greedy_until

+f19182ce697a2c095d9e5b56ee6659dc38c93994b69ca75d7c3d3f5fd87572b4
\ No newline at end of file

tests/testdata/math_algebra-v1-res.json

+{"results": {"math_algebra": {"acc": 0.0, "acc_stderr": 0.0}}, "versions": {"math_algebra": 1}}
\ No newline at end of file

tests/testdata/math_counting_and_prob-v1-greedy_until

+2aa9ae43ee9dbb2457525247d7b65358632c5eaa9cbfc40cf95a4f17f5d942ad
\ No newline at end of file

tests/testdata/math_counting_and_prob-v1-res.json

+{"results": {"math_counting_and_prob": {"acc": 0.0, "acc_stderr": 0.0}}, "versions": {"math_counting_and_prob": 1}}
\ No newline at end of file

tests/testdata/math_geometry-v1-greedy_until

+46bc4cb219b6903397da782699a684bdbb982c0c954ff82e6beeed5c84878f42
\ No newline at end of file

tests/testdata/math_geometry-v1-res.json

+{"results": {"math_geometry": {"acc": 0.0, "acc_stderr": 0.0}}, "versions": {"math_geometry": 1}}
\ No newline at end of file

tests/testdata/math_intermediate_algebra-v1-greedy_until

+d53c699de272d517ed7ad783b4e692302be9f9f97a8d4ac7a6541e538a7cabe0
\ No newline at end of file