merged with latest update from main

lm_eval/tasks/gsm8k/gsm8k-cot.yaml

@@ -41,3 +41,4 @@ filter_list:
       - function: "take_first"
 metadata:
   version: 2.0
+  num_fewshot: 8

lm_eval/tasks/gsm8k/gsm8k.yaml

@@ -24,6 +24,7 @@ generation_kwargs:
     - "\n\n"
     - "Question:"
   do_sample: false
+  temperature: 0.0
 repeats: 1
 num_fewshot: 5
 filter_list:

lm_eval/tasks/minerva_math/minerva_math_algebra.yaml

@@ -22,3 +22,4 @@ metric_list:
 num_fewshot: 0
 metadata:
   version: 1.0
+  num_fewshot: 4

lm_eval/tasks/mmlu/_generate_configs.py

@@ -85,13 +85,13 @@ if __name__ == "__main__":
 
     # get filename of base_yaml so we can `"include": ` it in our "other" YAMLs.
     base_yaml_name = os.path.split(args.base_yaml_path)[-1]
-    with open(args.base_yaml_path) as f:
+    with open(args.base_yaml_path, encoding="utf-8") as f:
         base_yaml = yaml.full_load(f)
 
     if args.cot_prompt_path is not None:
         import json
 
-        with open(args.cot_prompt_path) as f:
+        with open(args.cot_prompt_path, encoding="utf-8") as f:
             cot_file = json.load(f)
 
     ALL_CATEGORIES = []
@@ -120,7 +120,7 @@ if __name__ == "__main__":
 
         file_save_path = args.save_prefix_path + f"_{subject}.yaml"
         eval_logger.info(f"Saving yaml for subset {subject} to {file_save_path}")
-        with open(file_save_path, "w") as yaml_file:
+        with open(file_save_path, "w", encoding="utf-8") as yaml_file:
             yaml.dump(
                 yaml_dict,
                 yaml_file,
@@ -142,7 +142,7 @@ if __name__ == "__main__":
         file_save_path = args.save_prefix_path + ".yaml"
 
     eval_logger.info(f"Saving benchmark config to {file_save_path}")
-    with open(file_save_path, "w") as yaml_file:
+    with open(file_save_path, "w", encoding="utf-8") as yaml_file:
         yaml.dump(
             {
                 "group": f"mmlu_{args.task_prefix}"

lm_eval/tasks/model_written_evals/advanced_ai_risk/_generate_configs.py

@@ -9,7 +9,7 @@ def main() -> None:
     for task in tqdm(datasets.get_dataset_infos(dataset_path).keys()):
         file_name = f"{task}.yaml"
         try:
-            with open(f"{file_name}", "w") as f:
+            with open(f"{file_name}", "w", encoding="utf-8") as f:
                 f.write("# Generated by _generate_configs.py\n")
                 yaml.dump(
                     {

lm_eval/tasks/model_written_evals/persona/_generate_configs.py

@@ -9,7 +9,7 @@ def main() -> None:
     for task in tqdm(datasets.get_dataset_infos(dataset_path).keys()):
         file_name = f"{task}.yaml"
         try:
-            with open(f"{file_name}", "w") as f:
+            with open(f"{file_name}", "w", encoding="utf-8") as f:
                 f.write("# Generated by _generate_configs.py\n")
                 yaml.dump(
                     {

lm_eval/tasks/qasper/utils.py

@@ -50,7 +50,7 @@ def process_docs(dataset, set_answer_type="bool"):
                     obs_list["abstract"].append(abstract)
                     obs_list["question"].append(question)
                     obs_list["answer_type"].append(answer_type)
-                    if type(answer) == list:
+                    if isinstance(answer, list):
                         answer = ", ".join(answer)
                     obs_list["answer"].append(answer)
 

lm_eval/tasks/scrolls/scrolls.yaml

 group: scrolls
 task:
-  # - task: scrolls_qasper
-  #   class: !function task.Qasper
+  - task: scrolls_qasper
+    class: !function task.Qasper
   - task: scrolls_quality
     class: !function task.QuALITY
-  # - scrolls_narrativeqa
-  #   class: !function task.NarrativeQA
-  # - scrolls_contractnli
-  #   class: !function task.ContractNLI
-  # - scrolls_govreport
-  #   class: !function task.GovReport
-  # - scrolls_summscreenfd
-  #   class: !function task.SummScreenFD
-  # - scrolls_qmsum
-  #   class: !function task.QMSum
+  - task: scrolls_narrativeqa
+    class: !function task.NarrativeQA
+  - task: scrolls_contractnli
+    class: !function task.ContractNLI
+  - task: scrolls_govreport
+    class: !function task.GovReport
+  - task: scrolls_summscreenfd
+    class: !function task.SummScreenFD
+  - task: scrolls_qmsum
+    class: !function task.QMSum

lm_eval/tasks/scrolls/task.py

@@ -115,7 +115,9 @@ class _SCROLLSTask(Task):
     PRUNE_MAX_TOKENS = None
     PRUNE_NUM_PROC = None
 
-    def __post_init__(self):
+    def __init__(self):
+        super().__init__()
+        if self.DATASET_NAME is not None:
             self.metric = load_metric(_download_metric(), config_name=self.DATASET_NAME)
 
     def has_training_docs(self):
@@ -224,9 +226,10 @@ class _SCROLLSMultipleChoiceTask(_SCROLLSTask):
     def process_results(self, doc, results):
         gold = doc["gold"]
 
-        acc = 1.0 if np.argmax(results) == gold else 0.0
+        lls, _ = zip(*results)
+        acc = 1.0 if np.argmax(lls) == gold else 0.0
         completion_len = np.array([float(len(i)) for i in doc["choices"]])
-        acc_norm = 1.0 if np.argmax(results / completion_len) == gold else 0.0
+        acc_norm = 1.0 if np.argmax(lls / completion_len) == gold else 0.0
 
         return {
             "acc": acc,
@@ -279,7 +282,6 @@ class _SCROLLSSummaryTask(_SCROLLSTask):
         return f"{doc['input']}\n\nQuestion: What is a summary of the preceding text?\nAnswer:"
 
 
-# @register_task("scrolls_qasper")
 class Qasper(_SCROLLSTask):
     """A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers
     https://arxiv.org/abs/2105.03011
@@ -337,7 +339,6 @@ class Qasper(_SCROLLSTask):
             )
 
 
-# @register_task("scrolls_quality")
 class QuALITY(_SCROLLSMultipleChoiceTask):
     """QuALITY: Question Answering with Long Input Texts, Yes!
     https://arxiv.org/abs/2112.08608
@@ -366,7 +367,6 @@ class QuALITY(_SCROLLSMultipleChoiceTask):
         return [doc]
 
 
-# @register_task("scrolls_narrativeqa")
 class NarrativeQA(_SCROLLSTask):
     """The NarrativeQA Reading Comprehension Challenge
     https://arxiv.org/abs/1712.07040
@@ -400,7 +400,6 @@ class NarrativeQA(_SCROLLSTask):
         )
 
 
-# @register_task("scrolls_contractnli")
 class ContractNLI(_SCROLLSMultipleChoiceTask):
     """ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts
     https://arxiv.org/abs/1712.07040
@@ -419,7 +418,6 @@ class ContractNLI(_SCROLLSMultipleChoiceTask):
         return f"{doc['text']}\n\nHypothesis: {doc['question']}\nConclusion:"
 
 
-# @register_task("scrolls_govreport")
 class GovReport(_SCROLLSSummaryTask):
     """Efficient Attentions for Long Document Summarization
     https://arxiv.org/abs/2104.02112
@@ -433,7 +431,6 @@ class GovReport(_SCROLLSSummaryTask):
     DATASET_NAME = "gov_report"
 
 
-# @register_task("scrolls_summscreenfd")
 class SummScreenFD(_SCROLLSSummaryTask):
     """SummScreen: A Dataset for Abstractive Screenplay Summarization
     https://arxiv.org/abs/2104.07091
@@ -442,7 +439,6 @@ class SummScreenFD(_SCROLLSSummaryTask):
     DATASET_NAME = "summ_screen_fd"
 
 
-# @register_task("scrolls_qmsum")
 class QMSum(_SCROLLSSummaryTask):
     """QMSum: A New Benchmark for Query-based Multi-domain
     Meeting Summarization

lm_eval/tasks/squadv2/task.py

@@ -19,7 +19,7 @@ from math import exp
 from functools import partial
 from packaging import version
 
-from lm_eval.api.task import Task
+from lm_eval.api.task import ConfigurableTask
 from lm_eval.api.instance import Instance
 
 _CITATION = """
@@ -46,11 +46,14 @@ def _squad_agg(key, items):
     return _squad_metric(predictions=predictions, references=references).get(key, 0)
 
 
-class SQuAD2(Task):
+class SQuAD2(ConfigurableTask):
     VERSION = 3
     DATASET_PATH = "squad_v2"
     DATASET_NAME = None
 
+    def __init__(self):
+        super().__init__(config={'metadata': {'version': self.VERSION}})
+
     # HF changed squad on us so we have to make sure we aren't running the old one
     assert version.parse(datasets.__version__) >= version.parse(
         "1.11.0"

lm_eval/tasks/super_glue/wsc/t5-prompt.yaml

@@ -7,6 +7,7 @@ training_split: train
 validation_split: validation
 output_type: generate_until
 doc_to_text: !function "t5_utils.doc_to_text"
+process_results: !function "t5_utils.process_results"
 doc_to_target: label
 generation_kwargs:
   until:
@@ -15,9 +16,5 @@ metric_list:
   - metric: accuracy
     aggregation: mean
     higher_is_better: true
-filter_list:
-  - name: "wsc_postprocessor"
-    filter:
-      - function: !function t5_utils.WSCPostprocess
 metadata:
-  version: 0.0
+  version: 1.0

lm_eval/tasks/super_glue/wsc/t5_utils.py

 import re
-from lm_eval.api.filter import Filter
-
+from typing import List
 
 def doc_to_text(x):
     text = re.sub(r" X ", " *" + x["span2_text"] + "* ", _wsc_inputs(x))
@@ -24,7 +23,7 @@ def _wsc_inputs(x):
             [
                 " ".join(words[:pronoun_index]),
                 "X",
-                " ".join(words[pronoun_index + 1 :]),
+                " ".join(words[pronoun_index + 1:]),
             ]
         )
 
@@ -52,9 +51,7 @@ def _wsc_inputs(x):
     return create_input()
 
 
-class WSCPostprocess(Filter):
-    def __init__(self, **kwargs):
-        self.determiners = {
+DETERMINERS = {
     "a",
     "an",
     "few",
@@ -76,18 +73,18 @@ class WSCPostprocess(Filter):
     "which",
     "whose",
     "your",
-        }
+}
+
 
-    def clean(self, s):
+def clean(s: str) -> str:
     """Ignore capitalization and determiners."""
     s = s.strip().lower()
-        return " ".join([w for w in s.split(" ") if w not in self.determiners])
+    return " ".join([w for w in s.split(" ") if w not in DETERMINERS])
 
-    def apply(self, resps, docs):
-        filtered_resps = []
-        for prediction, reference in zip(*(resps, docs["span1_text"])):
-            prediction = self.clean(prediction[0])
-            reference = self.clean(reference)
+
+def process_results(docs: dict, resps: List):
+    prediction = clean(resps[0])
+    reference = clean(docs["span1_text"])
 
     if ("'" in prediction) != ("'" in reference):
         # referent is "Bob's hat" as predicting the referent.
@@ -102,6 +99,5 @@ class WSCPostprocess(Filter):
             referent_words
         ) or referent_words.issubset(prediction_words)
 
-            filtered_resps.append(predicted_referent)
-
-        return filtered_resps
+    acc = 1.0 if predicted_referent == docs["label"] else 0.0
+    return {"accuracy": acc}

lm_eval/tasks/xwinograd/utils.py

@@ -51,7 +51,7 @@ def gen_lang_yamls(output_dir: str, overwrite: bool) -> None:
     for lang in LANGUAGES:
         file_name = f"xwinograd_{lang}.yaml"
         try:
-            with open(f"{output_dir}/{file_name}", "w" if overwrite else "x") as f:
+            with open(f"{output_dir}/{file_name}", "w" if overwrite else "x", encoding="utf-8") as f:
                 f.write("# Generated by utils.py\n")
                 yaml.dump(
                     {

lm_eval/utils.py

@@ -501,14 +501,14 @@ def import_function(loader, node):
     return function
 
 
-def load_yaml_config(mode="simple", yaml_path=None, yaml_config=None, yaml_dir=None):
+def load_yaml_config(yaml_path=None, yaml_config=None, yaml_dir=None, mode="full"):
     if mode == "simple":
-        constuctor_fn = ignore_constructor
+        constructor_fn = ignore_constructor
     elif mode == "full":
-        constuctor_fn = import_function
+        constructor_fn = import_function
 
     # Add the import_function constructor to the YAML loader
-    yaml.add_constructor("!function", constuctor_fn)
+    yaml.add_constructor("!function", constructor_fn)
     if yaml_config is None:
         with open(yaml_path, "rb") as file:
             yaml_config = yaml.full_load(file)
@@ -536,7 +536,7 @@ def load_yaml_config(mode="simple", yaml_path=None, yaml_config=None, yaml_dir=N
                 path = os.path.join(yaml_dir, path)
 
             try:
-                included_yaml_config = load_yaml_config(mode=mode, yaml_path=path)
+                included_yaml_config = load_yaml_config(yaml_path=path, mode=mode)
                 final_yaml_config.update(included_yaml_config)
             except Exception as ex:
                 # If failed to load, ignore

pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "lm_eval"
-version = "0.4.0"
+version = "0.4.1"
 authors = [
     {name="EleutherAI", email="contact@eleuther.ai"}
 ]
@@ -56,15 +56,14 @@ Repository = "https://github.com/EleutherAI/lm-evaluation-harness"
 [project.optional-dependencies]
 anthropic = ["anthropic"]
 dev = ["pytest", "pytest-cov", "pytest-xdist", "pre-commit", "mypy"]
-gptq = ["auto-gptq[triton] @ git+https://github.com/PanQiWei/AutoGPTQ"]
+gptq = ["auto-gptq[triton]>=0.6.0"]
 ifeval = ["langdetect", "immutabledict"]
 mamba = ["mamba_ssm", "causal-conv1d==1.0.2"]
 math = ["sympy>=1.12", "antlr4-python3-runtime==4.11"]
 multilingual = ["nagisa>=0.2.7", "jieba>=0.42.1", "pycountry"]
 openai = ["openai==1.3.9", "tiktoken"]
-promptsource = [
-    "promptsource @ git+https://github.com/bigscience-workshop/promptsource.git#egg=promptsource"
-]
+optimum = ["optimum[openvino]"]
+promptsource = ["promptsource>=0.2.3"]
 sentencepiece = ["sentencepiece>=0.1.98", "protobuf>=4.22.1"]
 testing = ["pytest", "pytest-cov", "pytest-xdist"]
 vllm = ["vllm<=0.2.5"]

scripts/build_benchmark.py

@@ -23,7 +23,7 @@ def parse_args():
 if __name__ == "__main__":
     args = parse_args()
 
-    with open(args.benchmark_path) as file:
+    with open(args.benchmark_path, encoding="utf-8") as file:
         TASK_LIST = yaml.full_load(file)
         for task in tqdm(TASK_LIST):
             eval_logger.info(f"Processing {task}")
@@ -57,5 +57,5 @@ if __name__ == "__main__":
 
                 file_save_path = os.path.join(file_path, full_file_name)
                 eval_logger.info(f"Save to {file_save_path}")
-                with open(file_save_path, "w") as yaml_file:
+                with open(file_save_path, "w", encoding="utf-8") as yaml_file:
                     yaml.dump(config_dict, yaml_file)

scripts/clean_training_data/generate_13_grams.py

@@ -119,7 +119,7 @@ class Buckets:
 
 
 def do_ngrams_in_buckets(n_value, working_directory, bucket_count):
-    pile_statistics = json.load(open("pile_statistics.json", "r"))
+    pile_statistics = json.load(open("pile_statistics.json", "r", encoding="utf-8"))
     pile_document_count = pile_statistics["Document Count"]
     start_offsets = pile_statistics["File Start Offsets"]
 
@@ -212,4 +212,4 @@ if __name__ == "__main__":
 
     info_dict = {"title": "dataset ngrams", "ngram_size": 13}
     info_dict_path = os.path.join(args.working_directory, "info.json")
-    json.dump(info_dict, open(info_dict_path, "w"))
+    json.dump(info_dict, open(info_dict_path, "w", encoding="utf-8"))

scripts/clean_training_data/investigate_pile.py

@@ -79,7 +79,7 @@ if __name__ == "__main__":
 
     stats_file_path = "pile_statistics.json"
     if os.path.exists(stats_file_path):
-        stats = json.load(open(stats_file_path, "r"))
+        stats = json.load(open(stats_file_path, "r", encoding="utf-8"))
     else:
         document_count, total_document_size_chars, start_offsets = get_stats()
         stats = {
@@ -88,7 +88,7 @@ if __name__ == "__main__":
             "Total Pile Characters": total_document_size_chars,
             "File Start Offsets": start_offsets,
         }
-        json.dump(stats, open(stats_file_path, "w"), indent=4)
+        json.dump(stats, open(stats_file_path, "w", encoding="utf-8"), indent=4)
 
     print(f"document_count: {stats['Document Count']}")
     print(f"total_chars: {stats['Total Pile Characters']}")

scripts/make_table_results.py

@@ -61,14 +61,14 @@ if __name__ == "__main__":
         if not filenames:
             continue
         path_readme = os.path.join(dirpath, "README.md")
-        with open(path_readme, "w") as f:
+        with open(path_readme, "w", encoding="utf-8") as f:
             # get path name, only last folder
             path_name = dirpath.split("/")[-1]
             f.write(f"# {path_name} \n\n")
         for filename in sorted([f for f in filenames if f.endswith(".json")]):
             path = os.path.join(dirpath, filename)
-            with open(path, "r") as f:
+            with open(path, "r", encoding="utf-8") as f:
                 result_dict = json.load(f)
-            with open(path_readme, "a") as f:
+            with open(path_readme, "a", encoding="utf-8") as f:
                 f.write(f"## {filename} \n")
                 f.write(f"{make_table(result_dict)} \n")

scripts/make_table_tasks.py

@@ -50,5 +50,5 @@ if __name__ == "__main__":
         values.append(v)
     writer.value_matrix = values
     table = writer.dumps()
-    with open(args.output, "w") as f:
+    with open(args.output, "w", encoding="utf-8") as f:
         f.write(table)