merged with conflict resolved

lm_eval/prompts/__init__.py

+from lm_eval.logger import eval_logger
+from promptsource.templates import DatasetTemplates
+
 # TODO: decide whether we want jinja2 or f-string prompts. would it be cursed to support both?
 # Prompt library.
 # Stores prompts in a dictionary indexed by 2 levels:
@@ -6,17 +9,40 @@
 PROMPT_REGISTRY = {
     "qa-basic": {
         "question-newline-answer": "Question: {{question}}\nAnswer:",
-        "q-newline-a": "Q: {question}\nA:"
+        "q-newline-a": "Q: {{question}}\nA:",
     },
 }
 
-def get_prompt(prompt_id: str):
+
+def get_prompt(prompt_id: str, dataset_name=None, subset_name=None):
     # unpack prompt name
-    try:
     category_name, prompt_name = prompt_id.split(":")
-    except:
+    if subset_name is None:
+        dataset_full_name = dataset_name
+    else:
+        dataset_full_name = f"{dataset_name}-{subset_name}"
+    eval_logger.info(f"Loading prompt from {category_name} for {dataset_full_name}")
+    if category_name == "promptsource":
+        try:
+            if subset_name is None:
+                prompts = DatasetTemplates(dataset_name=dataset_name)
+            else:
+                prompts = DatasetTemplates(
+                    dataset_name=dataset_name, subset_name=subset_name
+                )
+        except Exception:
+            raise ValueError(f"{dataset_name} and {subset_name} not found")
+        if prompt_name in prompts.all_template_names:
+            return prompts[prompt_name]
+        else:
             raise ValueError(
-            f"expected only a single `:` as separator between \
-prompt category and name, but got `{prompt_id}` instead"
+                f"{prompt_name} not in prompt list {prompts.all_template_names}"
             )
+    else:
+        try:
             return PROMPT_REGISTRY[category_name][prompt_name]
+        except Exception:
+            raise ValueError(
+                f"expected only a single `:` as separator between \
+                prompt category and name, but got `{prompt_id}` instead"
+            )

lm_eval/tasks/arc.py

@@ -12,11 +12,11 @@ a co-occurrence method fail to answer correctly) and an Easy Set of 5,197 questi
 
 Homepage: https://allenai.org/data/arc
 """
-from lm_eval.api.task import MultipleChoiceTask, register_task
-from lm_eval.prompts import get_prompt
-
 from lm_eval import utils
+from lm_eval.prompts import get_prompt
+from lm_eval.api.task import MultipleChoiceTask
 
+from lm_eval.api.register import register_task, register_group
 
 _CITATION = """
 @article{Clark2018ThinkYH,
@@ -28,6 +28,8 @@ _CITATION = """
 }
 """
 
+
+@register_group("arc")
 @register_task("arc_easy")
 class ARCEasy(MultipleChoiceTask):
     VERSION = "2.0"
@@ -80,6 +82,7 @@ class ARCEasy(MultipleChoiceTask):
         return doc["query"]
 
 
+@register_group("arc")
 @register_task("arc_challenge")
 class ARCChallenge(ARCEasy):
     DATASET_PATH = "ai2_arc"

lm_eval/tasks/arc/README.md

+# ARC
+
+Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
+https://arxiv.org/pdf/1803.05457.pdf
+
+The ARC dataset consists of 7,787 science exam questions drawn from a variety
+of sources, including science questions provided under license by a research
+partner affiliated with AI2. These are text-only, English language exam questions
+that span several grade levels as indicated in the files. Each question has a
+multiple choice structure (typically 4 answer options). The questions are sorted
+into a Challenge Set of 2,590 “hard” questions (those that both a retrieval and
+a co-occurrence method fail to answer correctly) and an Easy Set of 5,197 questions.
+
+Homepage: https://allenai.org/data/arc
+
+### Citation
+```
+@article{Clark2018ThinkYH,
+  title={Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge},
+  author={Peter Clark and Isaac Cowhey and Oren Etzioni and Tushar Khot and Ashish Sabharwal and Carissa Schoenick and Oyvind Tafjord},
+  journal={ArXiv},
+  year={2018},
+  volume={abs/1803.05457}
+}
+```

lm_eval/tasks/arc.yaml → lm_eval/tasks/arc/arc_challenge.yaml

+group:
+  - arc_yaml
+task: arc_challenge_yaml
 dataset_path: ai2_arc
 dataset_name: ARC-Challenge
 output_type: multiple_choice
@@ -14,3 +17,6 @@ metric_list:
   - metric: acc_norm
     aggregation: mean
     higher_is_better: true
+  - metric: acc_mutual_info
+    aggregation: mean
+    higher_is_better: true

lm_eval/tasks/arc/arc_easy.yaml

+group:
+  - arc_yaml
+task: arc_easy_yaml
+dataset_path: ai2_arc
+dataset_name: ARC-Easy
+output_type: multiple_choice
+training_split: train
+validation_split: validation
+test_split: test
+template_aliases: "{% set answer_choices = choices['text'] %}{% set gold = choices.label.index(answerKey) %}" # set the list of possible answer choices, and set what this doc's gold answer is (set what ds column used, and what)
+doc_to_text: "Question: {{question}}\nAnswer:"
+doc_to_target: "{{gold}}" # this will be cast to an int.
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_mutual_info
+    aggregation: mean
+    higher_is_better: true

lm_eval/tasks/gsm8k.py

@@ -17,13 +17,14 @@ model's sample/generation function.
 Homepage: https://github.com/openai/grade-school-math
 """
 import re
-from lm_eval.api.task import Task, register_task
-from lm_eval.api.instance import Instance
+from lm_eval import utils
+from lm_eval.api.task import Task
 from lm_eval.api.metrics import mean
+from lm_eval.api.instance import Instance
 
-from lm_eval import utils
 from lm_eval.prompts import get_prompt
 
+from lm_eval.api.register import register_task, register_group
 
 _CITATION = """
 @misc{cobbe2021training,
@@ -88,7 +89,13 @@ class GradeSchoolMath8K(Task):
         """
         # NOTE: The paper implements "verifiers" that assign a score to multiple
         # solutions and output the highest ranked solution.
-        return Instance(request_type=self.OUTPUT_TYPE, doc=doc, arguments=(ctx, ["\n"]), idx=0, **kwargs)
+        return Instance(
+            request_type=self.OUTPUT_TYPE,
+            doc=doc,
+            arguments=(ctx, ["\n"]),
+            idx=0,
+            **kwargs
+        )
         # completion = rf.greedy_until(ctx, ["\n"])
         # return completion
 

lm_eval/tasks/gsm8k/cot-gsm8k.yaml

+# "Training Verifiers to Solve Math Word Problems"
+# https://arxiv.org/abs/2110.14168
+
+# State-of-the-art language models can match human performance on many tasks, but
+# they still struggle to robustly perform multi-step mathematical reasoning. To
+# diagnose the failures of current models and support research, we introduce GSM8K,
+# a dataset of 8.5K high quality linguistically diverse grade school math word problems.
+# We find that even the largest transformer models fail to achieve high test performance,
+# despite the conceptual simplicity of this problem distribution.
+
+# 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.
+
+# Homepage: https://github.com/openai/grade-school-math
+
+# _CITATION = """
+# @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}
+# }
+# """
+
+task: gsm8k_yaml
+dataset_path: gsm8k
+dataset_name: main
+training_split: train
+test_split: test
+use_prompt: "qa-basic:question-newline-answer"
+doc_to_target: "{{answer.split('### ')[-1]}}"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true
+delimiter: "\n"
+repeats: 4
+# filter_list:
+#   - name: "get-answer"
+#     filter:
+#       - function: "regex"
+#         regex_pattern: "#### (\-?[0-9\.\,]+)"

lm_eval/tasks/lambada.py

@@ -12,10 +12,11 @@ in the broader discourse.
 
 Homepage: https://zenodo.org/record/2630551#.X4Xzn5NKjUI
 """
-from lm_eval.api.task import Task, register_task
+from lm_eval.api.task import Task
 from lm_eval.api.instance import Instance
 from lm_eval.api.metrics import mean, perplexity
 
+from lm_eval.api.register import register_task, register_group
 
 _CITATION = """
 @misc{
@@ -59,11 +60,18 @@ class LambadaBase(Task):
         return " " + doc["text"].rsplit(" ", 1)[1]
 
     def construct_requests(self, doc, ctx, **kwargs):
-        return Instance(request_type=self.OUTPUT_TYPE, doc=doc, arguments=(ctx, self.doc_to_target(doc)), **kwargs)
+        return Instance(
+            request_type=self.OUTPUT_TYPE,
+            doc=doc,
+            arguments=(ctx, self.doc_to_target(doc)),
+            **kwargs
+        )
 
     def process_results(self, doc, results):
         # TODO: this ^ is a hack. filters should make it so that we only have one response per request that we score
-        results = results[0] # TODO: recheck this. currently a list of [(ll, is_greedy)] is passed in
+        results = results[
+            0
+        ]  # TODO: recheck this. currently a list of [(ll, is_greedy)] is passed in
         ll, is_greedy = results
 
         return {"ppl": ll, "acc": int(is_greedy)}
@@ -91,6 +99,7 @@ class LambadaStandard(LambadaBase):
     def has_test_docs(self):
         return True
 
+
 @register_task("lambada_openai")
 class LambadaOpenAI(LambadaBase):
     """The LAMBADA task using the LAMBADA OpenAI dataset, a modified version of the

lm_eval/tasks/lambada.yaml → lm_eval/tasks/lambada/base.yaml

+task: lambada_openai_yaml
 dataset_path: EleutherAI/lambada_openai
 dataset_name: default
 output_type: loglikelihood

lm_eval/tasks/pile.py

@@ -10,8 +10,9 @@ math, computer science, and philosophy papers.
 Homepage: https://pile.eleuther.ai/
 """
 
-from lm_eval.api.task import PerplexityTask, register_task
+from lm_eval.api.task import PerplexityTask
 
+from lm_eval.api.register import register_task, register_group
 
 _CITATION = """
 @article{pile,

lm_eval/tasks/pile_enron.yaml → lm_eval/tasks/pile/pile_enron.yaml

+# The Pile: An 800GB Dataset of Diverse Text for Language Modeling
+# https://arxiv.org/pdf/2101.00027.pdf
+
+# The Pile is a 825 GiB diverse, open source language modelling data set that consists
+# of 22 smaller, high-quality datasets combined together. To score well on Pile
+# BPB (bits per byte), a model must be able to understand many disparate domains
+# including books, github repositories, webpages, chat logs, and medical, physics,
+# math, computer science, and philosophy papers.
+# Homepage: https://pile.eleuther.ai/
+
+# _CITATION = """
+# @article{pile,
+#   title={The {P}ile: An 800GB Dataset of Diverse Text for Language Modeling},
+#   author={Gao, Leo and Biderman, Stella and Black, Sid and Golding, Laurence and Hoppe, Travis and Foster, Charles and Phang, Jason and He, Horace and Thite, Anish and Nabeshima, Noa and Presser, Shawn and Leahy, Connor},
+#   journal={arXiv preprint arXiv:2101.00027},
+#   year={2020}
+# }
+# """
+
+names:
+  - pile_enron_yaml
 dataset_path: EleutherAI/the_pile
 dataset_name: enron_emails
 output_type: loglikelihood_rolling

lm_eval/tasks/gsm8k.yaml → lm_eval/tasks/super_glue/boolq/promptsource-00.yaml

-dataset_path: gsm8k
-dataset_name: main
+group:
+  - super-glue-promptsource
+task: "GPT-3 Style"
+dataset_path: super_glue
+dataset_name: boolq
 training_split: train
-test_split: test
-doc_to_target: "{{answer.split('### ')[-1]}}"
-use_prompt: "qa-basic:question-newline-answer"
+validation_split: validation
+use_prompt: "promptsource:GPT-3 Style"
 metric_list:
   - metric: exact_match
     aggregation: mean
     higher_is_better: true
     ignore_case: true
     ignore_punctuation: true
-delimiter: "\n"
-# filters: [
-#   ["regex", ["regex", "take_first"]]
-# ]
\ No newline at end of file

lm_eval/tasks/super_glue/boolq/promptsource-01.yaml

+include: promptsource-00.yaml
+group:
+  - super-glue-promptsource
+task: "based on the previous passage"
+use_prompt: "promptsource:based on the previous passage"

lm_eval/tasks/super_glue/boolq/promptsource-02.yaml

+include: promptsource-00.yaml
+group:
+  - super-glue-promptsource
+task: "based on the following passage"
+use_prompt: "promptsource:based on the following passage"

lm_eval/tasks/sglue_cb.yaml → lm_eval/tasks/super_glue/cb/promptsource-00.yaml

+group:
+  - super-glue-promptsource
+task: "GPT-3 style"
 dataset_path: super_glue
 dataset_name: cb
 training_split: train
 validation_split: validation
-doc_to_text: "Suppose {{premise}} Can we infer that \"{{hypothesis}}\"? Yes, no, or maybe?"
-doc_to_target: "{% set answer_choices = ['Yes', 'No', 'Maybe'] %}{{answer_choices[label]}}"
+use_prompt: "promptsource:GPT-3 style"
 metric_list:
   - metric: exact_match
     aggregation: mean

lm_eval/tasks/super_glue/cb/promptsource-01.yaml

+include: promptsource-00.yaml
+group:
+  - super-glue-promptsource
+task: "MNLI crowdsource"
+use_prompt: "promptsource:MNLI crowdsource"

lm_eval/tasks/super_glue/cb/promptsource-02.yaml

+include: promptsource-00.yaml
+group:
+  - super-glue-promptsource
+task: "based on the previous passage"
+use_prompt: "promptsource:based on the previous passage"

examples/configurable_task/sglue_cb.yaml → lm_eval/tasks/super_glue/cb/t5-prompt.yaml

+group:
+  - super-glue-t5-prompt
+task: t5-prompt
+reference: "From Raffel et. al. 2019"
 dataset_path: super_glue
 dataset_name: cb
 training_split: train
 validation_split: validation
-template_aliases: "{% set hypo = hypothesis %}"
-doc_to_text: "Suppose {{premise}} Can we infer that \"{{hypo}}\"? Yes, no, or maybe?"
-doc_to_target: "{% set answer_choices = ['Yes', 'No', 'Maybe'] %}{{answer_choices[label]}}"
+doc_to_text: "cb hypothesis: {{hypothesis}} premise {{premise}}"
+doc_to_target: "{% set answer_choices = ['entailment', 'contradiction', 'neutral'] %}{{answer_choices[label]}}"
 metric_list:
   - metric: exact_match
     aggregation: mean

lm_eval/tasks/super_glue/copa/promptsource-00.yaml

+group:
+  - super-glue-promptsource
+task: "C1 or C2? premise, so/because…"
+dataset_path: super_glue
+dataset_name: copa
+training_split: train
+validation_split: validation
+use_prompt: "promptsource:C1 or C2? premise, so/because…"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true