Merge pull request #501 from EleutherAI/update-config

Update config

lm_eval/tasks/__init__.py

 import os
+from typing import List, Union
 
-from lm_eval.api.task import register_yaml_task
+from .arc import *
 
-from .vanilla import *
+from lm_eval import utils
+from lm_eval.logger import eval_logger
+from lm_eval.api.task import TaskConfig, Task, ConfigurableTask
+from lm_eval.api.register import (
+    register_task,
+    register_group,
+    task_registry,
+    group_registry,
+)
 
-# we want to register all yaml tasks in our .yaml folder.
-yaml_dir = os.path.dirname(os.path.abspath(__file__)) + "/" + "yaml"
 
+def get_task_name_from_config(task_config):
+    return "configurable_{dataset_path}_{dataset_name}".format(**task_config)
 
-for yaml in sorted(os.listdir(yaml_dir)):
-    yaml = os.path.join(yaml_dir, yaml)
-    register_yaml_task(yaml)
+
+task_dir = os.path.dirname(os.path.abspath(__file__)) + "/"
+for root, subdirs, file_list in os.walk(task_dir):
+    if (subdirs == []) and (len(file_list) > 0):
+        for file in file_list:
+            if "yaml" in file:
+                yaml_path = os.path.join(root, file)
+                try:
+                    config = utils.load_yaml_config(yaml_path)
+
+                    SubClass = type(
+                        config["task"] + "ConfigurableTask",
+                        (ConfigurableTask,),
+                        {"CONFIG": TaskConfig(**config)},
+                    )
+
+                    if "task" in config:
+                        task_name = "{}:{}".format(
+                            get_task_name_from_config(config), config["task"]
+                        )
+                        register_task(task_name)(SubClass)
+
+                    if "group" in config:
+                        for group in config["group"]:
+                            register_group(group)(SubClass)
+                except Exception as err:
+                    print(f"Unexpected {err=}, {type(err)=}")
+
+TASK_REGISTRY = task_registry
+GROUP_REGISTRY = group_registry
+ALL_TASKS = sorted(list(TASK_REGISTRY.keys()) + list(GROUP_REGISTRY.keys()))
+
+
+def get_task(task_name, config):
+    try:
+        return TASK_REGISTRY[task_name](config=config)
+    except KeyError:
+        eval_logger.info("Available tasks:")
+        eval_logger.info(TASK_REGISTRY)
+        raise KeyError(f"Missing task {task_name}")
+
+
+def get_task_name_from_object(task_object):
+    for name, class_ in TASK_REGISTRY.items():
+        if class_ is task_object:
+            return name
+
+    # TODO: scrap this
+    # this gives a mechanism for non-registered tasks to have a custom name anyways when reporting
+    return (
+        task_object.EVAL_HARNESS_NAME
+        if hasattr(task_object, "EVAL_HARNESS_NAME")
+        else type(task_object).__name__
+    )
+
+
+# TODO: pass num_fewshot and other cmdline overrides in a better way
+def get_task_dict(task_name_list: List[Union[str, dict, Task]], **kwargs):
+
+    config = {**kwargs}
+
+    task_name_from_registry_dict = {}
+    task_name_from_config_dict = {}
+    task_name_from_object_dict = {}
+
+    for task_element in task_name_list:
+        if isinstance(task_element, str):
+
+            if task_element in GROUP_REGISTRY:
+                for task_name in GROUP_REGISTRY[task_element]:
+                    if task_name not in task_name_from_registry_dict:
+                        task_name_from_registry_dict = {
+                            **task_name_from_registry_dict,
+                            task_name: get_task(task_name=task_name, config=config),
+                        }
+            else:
+                task_name = task_element
+                if task_name not in task_name_from_registry_dict:
+                    task_name_from_registry_dict = {
+                        **task_name_from_registry_dict,
+                        task_name: get_task(task_name=task_element, config=config),
+                    }
+
+        elif isinstance(task_element, dict):
+            task_element.update(config)
+            task_name_from_config_dict = {
+                **task_name_from_config_dict,
+                get_task_name_from_config(task_element): ConfigurableTask(
+                    config=task_element
+                ),
+            }
+
+        elif isinstance(task_element, Task):
+
+            task_name_from_object_dict = {
+                **task_name_from_object_dict,
+                get_task_name_from_object(task_element): task_element,
+            }
+
+    # task_name_from_registry_dict = {
+    #     task_name: get_task(
+    #         task_name=task_name,
+    #         task_config=config
+    #     )
+    #     for group_name in task_name_list for task_name in GROUP_REGISTRY[group_name]
+    #     if (isinstance(group_name, str)) and (group_name in GROUP_REGISTRY)
+    # }
+    # task_name_from_config_dict = {
+    #     get_task_name_from_config(task_config): ConfigurableTask(
+    #         config=task_config
+    #     )
+    #     for task_config in task_name_list
+    #     if isinstance(task_config, dict)
+    # }
+    # # TODO: Do we still need this?
+    # task_name_from_object_dict = {
+    #     get_task_name_from_object(task_object): task_object
+    #     for task_object in task_name_list
+    #     if isinstance(task_object, Task)
+    # }
+
+    assert set(task_name_from_registry_dict.keys()).isdisjoint(
+        set(task_name_from_object_dict.keys())
+    )
+    return {
+        **task_name_from_registry_dict,
+        **task_name_from_config_dict,
+        **task_name_from_object_dict,
+    }

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

-names:
-  - arc_challenge_yaml
+group:
+  - arc_yaml
+task: arc_challenge_yaml
 dataset_path: ai2_arc
 dataset_name: ARC-Challenge
 output_type: multiple_choice
@@ -19,6 +20,3 @@ metric_list:
   - metric: acc_mutual_info
     aggregation: mean
     higher_is_better: true
-  # - metric: exact_match
-  #   aggregation: mean
-  #   higher_is_better: true
\ No newline at end of file

lm_eval/tasks/yaml/arc_easy.yaml → lm_eval/tasks/arc/arc_easy.yaml

-names: 
-  - arc_easy_yaml
+group:
+  - arc_yaml
+task: arc_easy_yaml
 dataset_path: ai2_arc
 dataset_name: ARC-Easy
 output_type: multiple_choice
@@ -19,6 +20,3 @@ metric_list:
   - metric: acc_mutual_info
     aggregation: mean
     higher_is_better: true
-  # - metric: exact_match
-  #   aggregation: mean
-  #   higher_is_better: true
\ No newline at end of file

lm_eval/tasks/vanilla/gsm8k.py → 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/vanilla/lambada.py → 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/yaml/lambada.yaml → lm_eval/tasks/lambada/base.yaml

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

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

lm_eval/tasks/yaml/sglue_cb.yaml → lm_eval/tasks/super_glue/cb/based_on_previous_passage.yaml

-names: 
-  - sglue_cb_yamltest
+group:
+  - super-glue-cb
+task: based on the previous passage
+reference: "Adapted from the BoolQ prompts in Schick & Sch\xFCtze 2021."
 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_text: "{{premise}} Based on the previous passage, is it true that \"{{hypothesis}}\"? Yes, no, or maybe?"
 doc_to_target: "{% set answer_choices = ['Yes', 'No', 'Maybe'] %}{{answer_choices[label]}}"
 metric_list:
   - metric: exact_match

lm_eval/tasks/super_glue/cb/can_we_infer.yaml

+group:
+  - super-glue-cb
+include: based_on_previous_passage.yaml
+task: can we infer
+reference: Webson & Pavlick 2021
+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]}}"

lm_eval/tasks/super_glue/cb/claim_true_false_inconclusive.yaml

+group:
+  - super-glue-cb
+include: based_on_previous_passage.yaml
+task: claim true/false/inconclusive
+reference: Sanh et al. 2021
+doc_to_text: "{{premise}} Based on that information, is the claim: \"{{hypothesis}}\" \"true\", \"false\", or \"inconclusive\"?"
+doc_to_target: "{% set answer_choices = ['True', 'False', 'Inconclusive'] %}{{answer_choices[label]}}"

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/t5-prompt.yaml

+group:
+  - super-glue-t5-prompt
+task: t5-prompt
+reference: "From Raffel et. al. 2019"
+dataset_path: super_glue
+dataset_name: copa
+training_split: train
+validation_split: validation
+doc_to_text: "copa choice1: {{choice1}} choice2: {{choice2}} question: {{question}}"
+doc_to_target: "{% set answer_choices = ['False', 'True'] %}{{answer_choices[label]}}"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true

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

+group:
+  - super-glue-t5-prompt
+task: t5-prompt
+reference: "From Raffel et. al. 2019"
+dataset_path: super_glue
+dataset_name: record
+training_split: train
+validation_split: validation
+doc_to_text: "record query: {{query}} entities: {{entities}} passage: {{passage}}"
+doc_to_target: "{{answers}}"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true

lm_eval/tasks/yaml/gsm8k.yaml → lm_eval/tasks/super_glue/wsc.fixed/template-00.yaml

-names: 
-  - gsm8k_yaml
-dataset_path: gsm8k
-dataset_name: main
+group:
+  - t0-eval
+task: "does the pronoun refer to"
+dataset_path: super_glue
+dataset_name: wsc.fixed
 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:does the pronoun refer to"
 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/wsc.fixed/template-01.yaml

+group:
+  - t0-eval
+task: "by p they mean"
+dataset_path: super_glue
+dataset_name: wsc.fixed
+training_split: train
+validation_split: validation
+use_prompt: "promptsource:by p they mean"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true

lm_eval/tasks/super_glue/wsc.fixed/template-02.yaml

+group:
+  - t0-eval
+task: "in other words"
+dataset_path: super_glue
+dataset_name: wsc.fixed
+training_split: train
+validation_split: validation
+use_prompt: "promptsource:in other words"
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true
+    ignore_case: true
+    ignore_punctuation: true