Add proper normalization, checksum, and formatting

lm_eval/models/dummy.py

@@ -21,7 +21,8 @@ class DummyLM(LM):
     def greedy_until(self, requests):
         res = []
         
-        for _ in requests:
+        for ctx, _ in requests:
             res.append("lol")
+            assert ctx.strip() != ''
 
         return res

lm_eval/tasks/__init__.py

@@ -19,7 +19,7 @@ from . import naturalqs
 from . import sat
 from . import arithmetic
 from . import lambada
-from . import race 
+from . import race
 from . import piqa
 from . import triviaqa
 from . import pubmedqa
@@ -30,6 +30,7 @@ from . import translation
 from . import headqa
 from . import mathqa
 from . import drop
+from . import unscramble
 
 ########################################
 # Translation tasks
@@ -132,6 +133,13 @@ TASK_REGISTRY = {
     **translation.create_tasks_from_benchmarks(gpt3_translation_benchmarks),
     # chef's selection, mostly wmt20
     **translation.create_tasks_from_benchmarks(selected_translation_benchmarks),
+
+    # Word Scrambling and Manipulation Tasks
+    "anagrams1": unscramble.Anagrams1,
+    "anagrams2": unscramble.Anagrams2,
+    "cycle_letters": unscramble.CycleLetters,
+    "random_insertion": unscramble.RandomInsertion,
+    "reversed_words": unscramble.ReversedWords,
 }
 
 

lm_eval/tasks/drop.py

 import json
 import numpy as np
 import re
-import transformers.data.metrics.squad_metrics as squad_metrics
+import string
 from best_download import download_file
 from scipy.optimize import linear_sum_assignment
 from lm_eval.base import Task, rf
@@ -16,15 +16,18 @@ https://github.com/allenai/allennlp-reading-comprehension/blob/master/allennlp_r
 
 
 class DROP(Task):
-    DATAFOLDER = Path("data/drop")
-    URL = "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip"
+    DATASET_PATH = Path("data/drop")
 
     def download(self):
-        if self.DATAFOLDER.exists(): return
-        Path.mkdir(self.DATAFOLDER)
-        download_file(self.URL, to=str(self.DATAFOLDER / "drop_dataset.zip"))
-        with ZipFile(self.DATAFOLDER / "drop_dataset.zip", "r") as zip:
-            zip.extractall(self.DATAFOLDER)
+        if self.DATASET_PATH.exists():
+            return
+        Path.mkdir(self.DATASET_PATH)
+        url = "https://s3-us-west-2.amazonaws.com/allennlp/datasets/drop/drop_dataset.zip"
+        checksum = "39d2278a29fd729de301b111a45f434c24834f40df8f4ff116d864589e3249d6"
+        zip_path = self.DATASET_PATH / "drop_dataset.zip"
+        download_file(url, str(zip_path), checksum)
+        with ZipFile(zip_path, "r") as zip:
+            zip.extractall(self.DATASET_PATH)
 
     def has_training_docs(self):
         return True
@@ -61,16 +64,13 @@ class DROP(Task):
                           answers["date"]["year"]]).strip()]
 
     def training_docs(self):
-        docs = json.load(open(self.DATAFOLDER / "drop_dataset" / "drop_dataset_train.json"))
+        docs = json.load(open(self.DATASET_PATH / "drop_dataset" / "drop_dataset_train.json"))
         return self._load_docs([docs[k] for k in docs.keys()])
 
     def validation_docs(self):
-        docs = json.load(open(self.DATAFOLDER / "drop_dataset" / "drop_dataset_dev.json"))
+        docs = json.load(open(self.DATASET_PATH / "drop_dataset" / "drop_dataset_dev.json"))
         return self._load_docs([docs[k] for k in docs.keys()])
 
-    def test_docs(self):
-        pass
-
     def doc_to_text(self, doc):
         return f"Passage: {doc['passage']}\nQuestion: {doc['question']}\nAnswer:"
 
@@ -103,44 +103,55 @@ class DROP(Task):
         :param results:
             The results of the requests created in construct_requests.
         """
-        golds, preds = doc["answers"], results
-        exact_match = self._exact_match(golds, preds)
-        f1_score = self._f1_score(golds, preds)
+        preds, golds = results, doc["answers"]
+        exact_match, f1_score = self.get_metrics(preds, golds)
         return {
             "em": exact_match,
             "f1": f1_score
         }
 
-    def _exact_match(self, golds, preds):
-        """ Returns the exact match of normalized gold answers and predictions. """
-        normalized_golds = set([self._normalize(gold) for gold in golds])
-        normalized_preds = set([self._normalize(pred) for pred in preds])
-        return int(normalized_golds == normalized_preds)
+    def get_metrics(self, preds, golds):
+        exact_match = self._exact_match(preds, golds)
+        f1_score = self._f1_score(preds, golds)
+        return exact_match, f1_score
 
-    def _f1_score(self, golds, preds):
-        """Returns the average F1-score over normalized gold answers and predictions. """
-        gold_bags = self._answer_to_bags(golds)
+    def _exact_match(self, preds, golds):
+        """ Returns the exact match of normalized gold answers and predictions. """
+        normalized_preds = [self._normalize(pred) for pred in preds]
+        normalized_golds = [self._normalize(gold) for gold in golds]
+        is_equal_sets = set(normalized_preds) == set(normalized_golds)
+        is_equal_length = len(normalized_preds) == len(normalized_golds)
+        return int(is_equal_sets and is_equal_length)
+
+    def _f1_score(self, preds, golds):
+        """Returns the average F1-score over normalized gold answers and predictions.
+        From Section 5 of Dua et al. "DROP:...":
+        "When an answer has multiple spans, we first perform a one-to-one
+        alignment greedily based on bag-of-word overlap on the set of spans
+        and then compute average F1 over each span."
+        """
         pred_bags = self._answer_to_bags(preds)
-        f1_per_bag = self._align_bags(gold_bags, pred_bags)
+        gold_bags = self._answer_to_bags(golds)
+        f1_per_bag = self._align_bags(pred_bags, gold_bags)
         return np.mean(f1_per_bag)
 
     def _answer_to_bags(self, answers):
         return [set(self._normalize(answer).split()) for answer in answers]
 
-    def _align_bags(self, gold_bags, pred_bags):
+    def _align_bags(self, pred_bags, gold_bags):
         """ Returns the max metric value over all the answers. """
         scores = np.zeros([len(gold_bags), len(pred_bags)])
         for gold_index, gold_bag in enumerate(gold_bags):
             for pred_index, pred_bag in enumerate(pred_bags):
-                if self._is_number_match(gold_bag, pred_bag):
-                    scores[gold_index, pred_index] = self._bag_f1(gold_bag, pred_bag)
+                if self._is_number_match(pred_bag, gold_bag):
+                    scores[gold_index, pred_index] = self._bag_f1(pred_bag, gold_bag)
         row_ind, col_ind = linear_sum_assignment(-scores)
         max_scores = np.zeros([max(len(gold_bags), len(pred_bags))])
         for row, column in zip(row_ind, col_ind):
             max_scores[row] = max(max_scores[row], scores[row, column])
         return max_scores
 
-    def _bag_f1(self, gold_bag, pred_bag):
+    def _bag_f1(self, pred_bag, gold_bag):
         intersection = len(gold_bag.intersection(pred_bag))
         if intersection == 0:
             return 0.0
@@ -149,15 +160,45 @@ class DROP(Task):
         f1 = (2 * precision * recall) / (precision + recall)
         return f1
 
-    def _is_number_match(self, gold_bag, pred_bag):
-        gold_numbers = set(filter(lambda s: s.isnumeric(), list(gold_bag)))
-        pred_numbers = set(filter(lambda s: s.isnumeric(), list(pred_bag)))
-        return (not gold_numbers) or gold_numbers.intersection(pred_numbers)
+    def _is_number_match(self, pred_bag, gold_bag):
+        pred_numbers = set([word for word in pred_bag if self._is_number(word)])
+        gold_numbers = set([word for word in gold_bag if self._is_number(word)])
+        if (not gold_numbers) or gold_numbers.intersection(pred_numbers):
+            return True
+        return False
+
+    def _is_number(self, text):
+        try:
+            float(text)
+            return True
+        except ValueError:
+            return False
 
     def _normalize(self, answer):
+        def remove_articles(text):
+            regex = re.compile(r"\b(a|an|the)\b", re.UNICODE)
+            return re.sub(regex, " ", text)
+
+        def white_space_fix(text):
+            return " ".join(text.split())
+
+        def remove_punc(text):
+            exclude = set(string.punctuation)
+            if not self._is_number(text):
+                return "".join(ch for ch in text if ch not in exclude)
+            else:
+                return text
+
+        def fix_number(text):
+            return str(float(text)) if self._is_number(text) else text
+
         def tokenize(text):
             return re.split(" |-", text)
-        tokens = [squad_metrics.normalize_answer(token) for token in tokenize(answer)]
+
+        tokens = [
+            white_space_fix(remove_articles(fix_number(remove_punc(token.lower()))))
+            for token in tokenize(answer)
+        ]
         tokens = [token for token in tokens if token.strip()]
         normalized = " ".join(tokens).strip()
         return normalized

lm_eval/tasks/translation.py

@@ -145,12 +145,6 @@ class GeneralTranslationTask(Task):
         tar_lang = code_to_language(language_codes[1])
         return f"Translate these {src_lang} phrases to {tar_lang}."
 
-    # TODO This should be something like
-    #   French: {src_line}
-    #   English: {ref_line}
-    def fewshot_context(self, doc, num_fewshot, provide_description):
-        return ""
-
     def __str__(self):
         language_codes = self.sacrebleu_language_pair.split("-")
         src_lang = code_to_language(language_codes[0])

lm_eval/tasks/unscramble.py

+import gzip
+import json
+import random
+import shutil
+from pathlib import Path
+from best_download import download_file
+from lm_eval.base import Task, rf
+from lm_eval.metrics import mean
+
+
+def extract_gzip(gz, to):
+    with gzip.open(gz, 'rb') as fin:
+        with open(to, 'wb') as fout:
+            shutil.copyfileobj(fin, fout)
+
+
+class WordUnscrambleTask(Task):
+    BASE_PATH = Path("data/unscramble")
+    FILENAME = None
+    CHECKSUM = None  # SHA256 Checksum.
+
+    def __init__(self):
+        super().__init__()
+
+    def download(self):
+        if not self.BASE_PATH.exists():
+            Path.mkdir(self.BASE_PATH)
+        file = self.BASE_PATH / self.FILENAME
+        if not file.exists():
+            rawfile = file.parent / (file.name + ".gz")
+            base_url = "https://raw.githubusercontent.com/openai/gpt-3/master/data"
+            download_file(f"{base_url}/{self.FILENAME}.gz", str(rawfile), self.CHECKSUM)
+            extract_gzip(gz=rawfile, to=file)
+
+    def has_training_docs(self):
+        return False
+
+    def has_validation_docs(self):
+        return True
+
+    def has_test_docs(self):
+        return False
+
+    def validation_docs(self):
+        file = self.BASE_PATH / self.FILENAME
+        return (json.loads(line) for line in open(file).read().splitlines())
+
+    def fewshot_description(self):
+        return "Please unscramble the letters into a word, and write that word:"
+
+    def doc_to_text(self, doc):
+        return doc["context"]
+
+    def doc_to_target(self, doc):
+        return doc["completion"]
+
+    def construct_requests(self, doc, ctx):
+        completion = rf.greedy_until(ctx, ["\n"])
+        return completion
+
+    def process_results(self, doc, results):
+        pred = results[0]
+        gold = doc["completion"]
+        return {
+            "acc": int(pred == gold)
+        }
+
+    def aggregation(self):
+        return {
+            "acc": mean
+        }
+
+    def higher_is_better(self):
+        return {
+            "acc": True
+        }
+
+
+class Anagrams1(WordUnscrambleTask):
+    FILENAME = "mid_word_1_anagrams.jsonl"
+    CHECKSUM = "6768a86896083199de4815d4964cb2f6f1046476cfd80c2a562784f182905979"
+
+
+class Anagrams2(WordUnscrambleTask):
+    FILENAME = "mid_word_2_anagrams.jsonl"
+    CHECKSUM = "c3d839d09a7954b78a27cd2cd75d4ed0488656c56ef4dbd741a005343826cb01"
+
+
+class CycleLetters(WordUnscrambleTask):
+    FILENAME = "cycle_letters_in_word.jsonl"
+    CHECKSUM = "1689c9002bb8c5988bf5f05e977c9db92f57932c1b5a38998c29ac0dd71e1d42"
+
+
+class RandomInsertion(WordUnscrambleTask):
+    FILENAME = "random_insertion_in_word.jsonl"
+    CHECKSUM = "72e65d83da53d15752ee0c47379509de149ddbad32d61184e5991df29616b78a"
+
+
+class ReversedWords(WordUnscrambleTask):
+    FILENAME = "reversed_words.jsonl"
+    CHECKSUM = "133a08f875cd6c1ef8608a3233571a773881cc27b1c707de738cc6543439332a"

scripts/fewshot_description_experiment.py

+import argparse
+import json
+import numpy as np
+import random
+import itertools
+import collections
+import logging
+
+from lm_eval import models, tasks, evaluator, base
+
+logging.getLogger("openai").setLevel(logging.WARNING)
+
+
+fewshot_descriptions = [
+    "foo",
+    "bar"
+]
+
+task = "lambada"
+num_fewshot = 0
+model = "gpt2"
+model_args = ""
+limit = None
+no_cache = False
+
+
+class CustomDescTask:
+    def __init__(self, task, desc):
+        self.task = task
+        self.desc = desc
+
+        def fewshot_description():
+            return self.desc
+        
+        self.task.fewshot_description = fewshot_description
+
+    def __getattr__(self, attr):
+        return getattr(self.task, attr)
+
+
+def main():
+    random.seed(42)
+    np.random.seed(42)
+
+    lm = models.get_model(model).create_from_arg_string(model_args)
+    
+    if limit:
+        print("WARNING: --limit SHOULD ONLY BE USED FOR TESTING. REAL METRICS SHOULD NOT BE COMPUTED USING LIMIT.")
+
+    if not no_cache:
+        lm = base.CachingLM(lm, 'lm_cache/' + model + '_' + model_args.replace('=', '-').replace(',', '_') + '.db')
+
+    task_dict = tasks.get_task_dict([task])
+
+    for desc in fewshot_descriptions:
+        custom_task_dict = {k: CustomDescTask(v, desc) for k, v in task_dict.items()}
+
+        results = evaluator.evaluate(lm, custom_task_dict, True, num_fewshot, limit)
+
+        dumped = json.dumps(results, indent=2)
+
+        print('Description:', desc)
+        print(dumped)
+
+        # MAKE TABLE
+        from pytablewriter import MarkdownTableWriter
+
+        writer = MarkdownTableWriter()
+        writer.headers = ["Task", "Metric", "Value"]
+
+        values = []
+
+        for k, dic in results.items():
+            for m, v in dic.items():
+                values.append([k, m, '%.4f' % v])
+                k = ""
+        writer.value_matrix = values
+
+        print(writer.dumps())
+
+
+if __name__ == "__main__":
+    main()