Merge remote-tracking branch 'upstream/big-refactor' into big-refactor-autobatching

lm_eval/tasks/webqs/README.md

+# Task-name
+
+### Paper
+
+Title: `Semantic Parsing on Freebase from Question-Answer Pairs`
+
+Abstract: `https://cs.stanford.edu/~pliang/papers/freebase-emnlp2013.pdf`
+
+WebQuestions is a benchmark for question answering. The dataset consists of 6,642
+question/answer pairs. The questions are supposed to be answerable by Freebase, a
+large knowledge graph. The questions are mostly centered around a single named entity.
+The questions are popular ones asked on the web (at least in 2013).
+
+Homepage: `https://worksheets.codalab.org/worksheets/0xba659fe363cb46e7a505c5b6a774dc8a`
+
+
+### Citation
+
+```
+@inproceedings{berant-etal-2013-semantic,
+    title = "Semantic Parsing on {F}reebase from Question-Answer Pairs",
+    author = "Berant, Jonathan  and
+      Chou, Andrew  and
+      Frostig, Roy  and
+      Liang, Percy",
+    booktitle = "Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing",
+    month = oct,
+    year = "2013",
+    address = "Seattle, Washington, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/D13-1160",
+    pages = "1533--1544",
+}
+```
+
+### Subtasks
+
+List or describe tasks defined in this folder, and their names here:
+* `webqs`: `Questions with multiple accepted answers.`
+
+### Checklist
+
+For adding novel benchmarks/datasets to the library:
+  * [x] Is the task an existing benchmark in the literature?
+  * [x] Have you referenced the original paper that introduced the task?
+  * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
+
+
+If other tasks on this dataset are already supported:
+* [ ] Is the "Main" variant of this task clearly denoted?
+* [ ] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [ ] Have you noted which, if any, published evaluation setups are matched by this variant?

lm_eval/tasks/webqs/utils.py

+from typing import Dict, List
+
+
+def doc_to_choice(doc: Dict) -> List[str]:
+    """Return all of the accepted answers as choices."""
+    return _remove_prefixes(doc["answers"])
+
+
+def doc_to_target(doc: Dict) -> List[int]:
+    """Return list of indices of accepted answers (all of them)."""
+    remaining = _remove_prefixes(doc["answers"])
+    return list(range(len(remaining)))
+
+
+def _remove_prefixes(aliases):
+    """
+    Remove any alias that has a strict prefix elsewhere in the list.
+
+    This is an optimization. We can do this because if the prefix is acceptable by isgreedy,
+    we can stop looking.
+    """
+    aliases.sort()
+    ret = [aliases[0]]
+    for alias in aliases[1:]:
+        if not alias.startswith(ret[-1]):
+            ret.append(alias)
+    return ret

lm_eval/tasks/webqs/webqs.yaml

+group:
+  - freebase
+  - question_answer
+task: webqs
+dataset_path: web_questions
+dataset_name: null
+output_type: multiple_choice
+training_split: train
+validation_split: null
+test_split: test
+doc_to_text: "Question: {{question}}\nAnswer:"
+doc_to_target: !function utils.doc_to_target
+doc_to_choice: !function utils.doc_to_choice
+should_decontaminate: true
+doc_to_decontamination_query: question
+metric_list:
+  - metric: exact_match
+    aggregation: mean
+    higher_is_better: true

lm_eval/tasks/winogrande/preprocess_winogrande.py

-def partial_context(doc, option):
-    # Substitute the pronoun in the sentence with the specified option
-    # and ignore everything after.
-    pronoun_loc = doc["sentence"].index("_")
-    return doc["sentence"][:pronoun_loc] + option
+def doc_to_text(doc):
+    answer_to_num = {"1": 0, "2": 1}
+    return answer_to_num[doc["answer"]]
 
-def partial_target(doc):
-    # The target is everything after the document specified pronoun.
-    pronoun_loc = doc["sentence"].index("_") + 1
-    return doc["sentence"][pronoun_loc:].strip()
 
-def create_choices(doc):
-    choices = []
-    for option in [doc["option1"], doc["option2"]]:
-        partial_ctx = partial_context(doc, option)
-        choices.append(partial_ctx)
-    return choices
+def doc_to_target(doc):
+    idx = doc["sentence"].index("_") + 1
+    return doc["sentence"][idx:].strip()
 
-def gold_alias(doc):
-    answer_to_num = {"1": 0, "2": 1}
-    return answer_to_num[doc['answer']]
+
+def doc_to_choice(doc):
+    idx = doc["sentence"].index("_")
\ No newline at end of file
+    options = [doc["option1"], doc["option2"]]
+    return [doc["sentence"][:idx] + opt for opt in options]

lm_eval/tasks/winogrande/winogrande.yaml

 task: winogrande
 dataset_path: winogrande
 dataset_name: winogrande_xl
-output_type: winograd_schema
+output_type: multiple_choice
 training_split: train
 validation_split: validation
-doc_to_target: !function preprocess_winogrande.partial_target
-doc_to_text: "{{sentence}}"
-create_choices: !function preprocess_winogrande.create_choices
-gold_alias: !function preprocess_winogrande.gold_alias
+doc_to_text: !function preprocess_winogrande.doc_to_text
+doc_to_target: !function preprocess_winogrande.doc_to_target
+doc_to_choice: !function preprocess_winogrande.doc_to_choice
 metric_list:
   - metric: acc
     aggregation: mean

main.py

@@ -5,6 +5,7 @@ import fnmatch
 import jsonlines
 import argparse
 import logging
+from pathlib import Path
 
 from lm_eval import evaluator, utils
 from lm_eval.api.registry import ALL_TASKS
@@ -15,22 +16,41 @@ os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 def parse_args():
     parser = argparse.ArgumentParser()
-    parser.add_argument("--model", required=True)
-    parser.add_argument("--model_args", default="")
+    parser.add_argument("--model", required=True, help="Name of model e.g. `hf`")
+    parser.add_argument(
+        "--model_args",
+        default="",
+        help="String arguments for model, e.g. `pretrained=EleutherAI/pythia-160m,dtype=float32`",
+    )
     parser.add_argument(
         "--tasks", default=None, choices=utils.MultiChoice(sorted(ALL_TASKS))
     )
-    parser.add_argument("--config", default=None)
-    parser.add_argument("--num_fewshot", type=int, default=0)
-    parser.add_argument("--batch_size", type=str, default=1)
+    parser.add_argument(
+        "--num_fewshot",
+        type=int,
+        default=0,
+        help="Number of examples in few-shot context",
+    )
+    parser.add_argument("--batch_size", type=int, default=1)  # TODO: only integers
     parser.add_argument(
         "--max_batch_size",
         type=int,
         default=None,
         help="Maximal batch size to try with --batch_size auto",
     )
-    parser.add_argument("--device", type=str, default=None)
-    parser.add_argument("--output_path", default=None)
+    parser.add_argument(
+        "--device",
+        type=str,
+        default=None,
+        help="Device to use (e.g. cuda, cuda:0, cpu)",
+    )
+    parser.add_argument(
+        "--output_path",
+        default=None,
+        type=str,
+        metavar="= [dir/file.jsonl] [DIR]",
+        help="The path to the output file where the result metrics will be saved. If the path is a directory and log_samples is true, the results will be saved in the directory. Else the parent directory will be used.",
+    )
     parser.add_argument(
         "--limit",
         type=float,
@@ -38,11 +58,30 @@ def parse_args():
         help="Limit the number of examples per task. "
         "If <1, limit is a percentage of the total number of examples.",
     )
-    parser.add_argument("--data_sampling", type=float, default=None)
-    parser.add_argument("--use_cache", type=str, default=None)
-    parser.add_argument("--decontamination_ngrams_path", default=None)
-    parser.add_argument("--check_integrity", action="store_true")
-    parser.add_argument("--write_out", action="store_true", default=False)
+    parser.add_argument(
+        "--use_cache",
+        type=str,
+        default=None,
+        help="A path to a sqlite db file for caching model responses. `None` if not caching.",
+    )
+    parser.add_argument("--decontamination_ngrams_path", default=None)  # TODO: not used
+    parser.add_argument(
+        "--check_integrity",
+        action="store_true",
+        help="Whether to run the relevant part of the test suite for the tasks",
+    )
+    parser.add_argument(
+        "--write_out",
+        action="store_true",
+        default=False,
+        help="Prints the prompt for the first few documents",
+    )
+    parser.add_argument(
+        "--log_samples",
+        action="store_true",
+        default=False,
+        help="If True, write out all model outputs and documents for per-sample measurement and post-hoc analysis",
+    )
     return parser.parse_args()
 
 
@@ -74,6 +113,25 @@ def main():
                     config = utils.load_yaml_config(task)
                     task_names.append(config)
 
+    if args.output_path:
+        path = Path(args.output_path)
+        # check if file or 'dir/results.json' exists
+        if path.is_file() or Path(args.output_path).joinpath("results.json").is_file():
+            eval_logger.warning(
+                f"File already exists at {path}. Results will be overwritten."
+            )
+            assert not path.is_file(), "File already exists"
+        # if path json then get parent dir
+        elif path.suffix in (".json", ".jsonl"):
+            output_path_file = path
+            path.parent.mkdir(parents=True, exist_ok=True)
+            path = path.parent
+        else:
+            path.mkdir(parents=True, exist_ok=True)
+            output_path_file = path.joinpath("results.json")
+    elif args.log_samples and not args.output_path:
+        assert args.output_path, "Specify --output_path"
+
     eval_logger.info(f"Selected Tasks: {task_names}")
 
     results = evaluator.simple_evaluate(
@@ -89,34 +147,29 @@ def main():
         decontamination_ngrams_path=args.decontamination_ngrams_path,
         check_integrity=args.check_integrity,
         write_out=args.write_out,
+        log_samples=args.log_samples,
     )
 
     if results is not None:
-        samples = results.pop("samples")
+        if args.log_samples:
+            samples = results.pop("samples")
         dumped = json.dumps(results, indent=2, default=lambda o: str(o))
         print(dumped)
 
         batch_sizes = ",".join(map(str, results["config"]["batch_sizes"]))
 
         if args.output_path:
-            os.makedirs(os.path.dirname(args.output_path), exist_ok=True)
-
-            with open(args.output_path, "w") as f:
-                f.write(dumped)
-
-            for task_name, config in results["configs"].items():
-                output_name = "{}_{}".format(
-                    re.sub("/", "__", args.model_args), task_name
-                )
-                if os.path.isdir(args.output_path):
-                    filename = f"./{args.output_path}/{output_name}.jsonl"
-                elif os.path.isfile(args.output_path):
-                    filename = (
-                        f"./{os.path.dirname(args.output_path)}/{output_name}.jsonl"
+            output_path_file.open("w").write(dumped)
+
+            if args.log_samples:
+                for task_name, config in results["configs"].items():
+                    output_name = "{}_{}".format(
+                        re.sub("/", "__", args.model_args), task_name
                     )
+                    filename = path.joinpath(f"{output_name}.jsonl")
 
-                with jsonlines.open(filename, "w") as f:
-                    f.write_all(samples[task_name])
+                    with jsonlines.open(filename, "w") as f:
+                        f.write_all(samples[task_name])
 
         print(
             f"{args.model} ({args.model_args}), limit: {args.limit}, num_fewshot: {args.num_fewshot}, "

scripts/write_out.py

@@ -58,7 +58,6 @@ def main():
                 ctx = task.fewshot_context(
                     doc=doc,
                     num_fewshot=args.num_fewshot,
-                    rnd=rnd,
                 )
                 f.write(ctx + "\n")
 

setup.py

@@ -50,6 +50,13 @@ setuptools.setup(
     ],
     extras_require={
         "dev": ["black", "flake8", "pre-commit", "pytest", "pytest-cov"],
+        "linting": [
+            "flake8",
+            "pylint",
+            "mypy",
+            "pre-commit",
+        ],
+        "testing": ["pytest", "pytest-cov", "pytest-xdist"],
         "multilingual": ["nagisa>=0.2.7", "jieba>=0.42.1"],
         "sentencepiece": ["sentencepiece>=0.1.98", "protobuf>=4.22.1"],
         "promptsource": [

tests/conftest.py

+def pytest_addoption(parser):
+    parser.addoption(
+        "--new_task",
+        action="store_true",
+        help="new_tasks_found",
+    )

tests/extra/test_utils.py

+import json
+from typing import List
+from lm_eval.utils import load_yaml_config
+from pathlib import Path
+
+
+FILE_PATH = file_path = ".github/outputs/tasks_all_changed_and_modified_files.txt"
+
+
+def load_changed_files(file_path: str = FILE_PATH) -> List[str]:
+    with open(file_path, "r") as f:
+        return [line.strip() for line in f.readlines()]
+
+
+def parser(full_path: List[str]) -> List[str]:
+    _output = set()
+    for x in full_path:
+        if x.endswith(".yaml"):
+            _output.add(load_yaml_config(x)["task"])
+        elif x.endswith(".py"):
+            path = [str(x) for x in (list(Path(x).parent.glob("*.yaml")))]
+            _output |= {load_yaml_config(x)["task"] for x in path}
+    return list(_output)

tests/test_evaluator.py

@@ -5,7 +5,7 @@ import lm_eval.api.registry as registry
 import lm_eval.tasks as tasks
 
 # import lm_eval.models as models
-
+import lm_eval.api as api
 import lm_eval.evaluator as evaluator
 import random
 import pytest
@@ -15,60 +15,52 @@ import pytest
 # test once we break evaluator into smaller, more manageable pieces
 
 
-@pytest.mark.parametrize("taskname,task_class", tasks.TASK_REGISTRY.items())
-def test_evaluator(taskname, task_class):
-    task_dict = tasks.get_task_dict([taskname])
-
-    # TODO: re-add cachingLM
-    # os.system("rm test_cache.db")
-    # lm = base.CachingLM(models.get_model("dummy")(), "test_cache.db")
-    lm = registry.get_model("dummy")()
-
-    def ll_fn(reqs):
-        for ctx, cont in reqs:
-            if len(ctx) == 0:
-                continue
-            # space convention
-            assert ctx[-1] != " "
-            assert cont[0] == " " or ctx[-1] == "\n"
-
-        res = []
-
-        random.seed(42)
-        for _ in reqs:
-            res.append((-random.random(), False))
-
-        return res
-
-    def ll_perp_fn(reqs):
-        for (string,) in reqs:
-            assert isinstance(string, str)
-
-        res = []
-        random.seed(42)
-        for _ in reqs:
-            res.append(-random.random())
-
-        return res
-
-    lm.loglikelihood = ll_fn
-    lm.loglikelihood_rolling = ll_perp_fn
-
+@pytest.mark.parametrize(
+    "task_name,limit,model,model_args",
+    [
+        (
+            ["arc_easy"],
+            10,
+            "hf",
+            "pretrained=EleutherAI/pythia-160m,dtype=float32,device=cpu",
+        )
+    ],
+)
+def test_evaluator(task_name: list[str], limit: int, model: str, model_args: str):
+    task_name = task_name
     limit = 10
-    e1 = evaluator.evaluate(
-        lm=lm,
-        task_dict=task_dict,
-        num_fewshot=0,
+
+    e1 = evaluator.simple_evaluate(
+        model=model,
+        tasks=task_name,
         limit=limit,
-        bootstrap_iters=10,
+        model_args=model_args,
+    )
+    assert e1 is not None
+
+    lm = api.registry.get_model(model).create_from_arg_string(
+        model_args,
+        {
+            "batch_size": None,
+            "max_batch_size": None,
+            "device": None,
+        },
     )
+    task_dict = tasks.get_task_dict(task_name, num_fewshot=0)
+
     e2 = evaluator.evaluate(
         lm=lm,
         task_dict=task_dict,
-        num_fewshot=0,
         limit=limit,
-        bootstrap_iters=10,
     )
 
+    assert e2 is not None
     # check that caching is working
-    assert e1 == e2
+
+    def r(x):
+        return x["results"]["arc_easy"]
+
+    assert all(
+        x == y
+        for x, y in zip([y for _, y in r(e1).items()], [y for _, y in r(e2).items()])
+    )

tests/test_tasks.py

-import lm_eval.tasks as tasks
-
 import pytest
 from itertools import islice
+import lm_eval.tasks as tasks
+from tests.extra.test_utils import load_changed_files, parser
+from typing import List, ClassVar
+import os
 
 
-@pytest.mark.parametrize("taskname,task_class", tasks.TASK_REGISTRY.items())
-def test_basic_interface(taskname, task_class):
-    print("Evaluating task", taskname)
-    task = task_class()
+@pytest.fixture()
+def any_new_tasks(request) -> bool:
+    return request.config.getoption("--new_task")
 
-    assert task.has_training_docs() in [True, False]
-    assert task.has_validation_docs() in [True, False]
-    assert task.has_test_docs() in [True, False]
 
-    assert isinstance(task.aggregation(), dict)
-    assert isinstance(task.higher_is_better(), dict)
-    assert task.aggregation().keys() == task.higher_is_better().keys()
+# ["arc_easy] else get list of new tasks
+def new_tasks(any_new_tasks: bool) -> List[str]:
+    FILENAME = ".github/outputs/tasks_all_changed_and_modified_files.txt"
+    if any_new_tasks and os.path.exists(FILENAME):
+        return [parser(load_changed_files(FILENAME))]
+    elif os.getenv("API") is not None:
+        return ["arc_easy", "hellaswag", "piqa", "wikitext"]
+    else:
+        return ["arc_easy"]
 
-    for v in task.higher_is_better().values():
-        assert v in [True, False]
 
-    assert isinstance(task.VERSION, int)
+@pytest.fixture(params=new_tasks(any_new_tasks))
+def task_class(request):
+    task_name = request.param
+    return [cls for name, cls in tasks.TASK_REGISTRY.items() if name in task_name][0]
 
-    # test deterministic docs
-    # (don't test train because it's slow)
 
-    task2 = task_class()
+@pytest.fixture()
+def limit(any_new_tasks: bool) -> int:
+    return 100 if any_new_tasks else 10
 
-    limit = None
 
-    if taskname in ["triviaqa"] or taskname.startswith("pile_"):
-        limit = 10000
-    if task.has_validation_docs():
-        arr = list(islice(task.validation_docs(), limit))
-        arr2 = list(islice(task2.validation_docs(), limit))
+# Tests
 
-        assert arr == arr2
 
-        reqs = [task.construct_requests(doc, task.doc_to_text(doc)) for doc in arr]
-        reqs2 = [task2.construct_requests(doc, task2.doc_to_text(doc)) for doc in arr2]
+def test_download(task_class):
+    task_class().download()
+    assert task_class().dataset is not None
 
-        assert reqs == reqs2
 
-    if task.has_test_docs():
-        arr = list(islice(task.test_docs(), limit))
-        arr2 = list(islice(task2.test_docs(), limit))
+def test_has_training_docs(task_class):
+    assert task_class().has_training_docs() in [True, False]
 
-        assert arr == arr2
 
-        reqs = [task.construct_requests(doc, task.doc_to_text(doc)) for doc in arr]
-        reqs2 = [task2.construct_requests(doc, task2.doc_to_text(doc)) for doc in arr2]
+def test_check_training_docs(task_class):
+    task = task_class()
+    assert task.has_training_docs() if task._config["training_split"] else True
+
 
-        assert reqs == reqs2
+def test_has_validation_docs(task_class):
+    assert task_class().has_training_docs() in [True, False]
 
-    if task.has_training_docs():
-        arr = list(islice(task.training_docs(), limit))
-        arr2 = list(islice(task2.training_docs(), limit))
 
-        assert arr == arr2
+def test_check_validation_docs(task_class):
+    task = task_class()
+    assert (
+        task_class().has_training_docs() if task._config["validation_split"] else True
+    )
 
-        reqs = [task.construct_requests(doc, task.doc_to_text(doc)) for doc in arr]
-        reqs2 = [task2.construct_requests(doc, task2.doc_to_text(doc)) for doc in arr2]
 
-        assert reqs == reqs2
+def test_has_test_docs(task_class):
+    assert task_class().has_training_docs() in [True, False]
 
 
-@pytest.mark.parametrize("taskname,task_class", tasks.TASK_REGISTRY.items())
-def test_documents_and_requests(taskname, task_class):
-    print("Evaluating task", taskname)
+def test_check_test_docs(task_class):
     task = task_class()
-    fns = []
-    if task.has_training_docs():
-        fns.append(task.training_docs)
-    if task.has_validation_docs():
-        fns.append(task.validation_docs)
-    # test doc might not have labels
-    # if task.has_test_docs(): fns.append(task.test_docs)
-
-    for fn in fns:
-        # print(list(islice(fn(), 10)))
-        for doc in islice(fn(), 10):
-
-            txt = task.doc_to_text(doc)
-            tgt = task.doc_to_target(doc)
-
-            assert isinstance(txt, str)
-            assert isinstance(tgt, str)
-
-            # space convention
-            # allow txt to have length 0 for perplexity-like tasks since the model tacks an <|endoftext|> on
-            if len(txt) != 0:
-                assert txt[-1] != " "
-                assert tgt[0] == " " or txt[-1] == "\n"
-
-            reqs = task.construct_requests(doc, txt)
-
-            # construct_requests can return just one request
-            if not isinstance(reqs, (list, tuple)):
-                reqs = [reqs]
-
-            # todo: mock lm after refactoring evaluator.py to not be a mess
-            # for req in reqs:
-            #     assert isinstance(req, base.Request)
+    assert task_class().has_training_docs() if task._config["test_split"] else True
+
+
+def test_should_decontaminate(task_class):
+    task_class = task_class()
+    assert task_class.should_decontaminate() in [True, False]
+    if task_class.should_decontaminate():
+        assert task_class._config["doc_to_decontamination_query"] is not None
+
+
+def test_doc_to_text(task_class, limit):
+    arr = (
+        list(islice(task_class().test_docs(), limit))
+        if limit
+        else list(task_class().test_docs())
+    )
+    _array = [task_class().doc_to_text(doc) for doc in arr]
+    # space convention; allow txt to have length 0 for perplexity-like tasks since the model tacks an <|endoftext|> on
+    assert all(
+        isinstance(x, str) and (x[-1] != " " if len(x) != 0 else True) for x in _array
+    )
+
+
+def test_create_choices(task_class, limit):
+    arr = (
+        list(islice(task_class().test_docs(), limit))
+        if limit
+        else list(task_class().test_docs())
+    )
+    _array = [task_class().doc_to_choice(doc) for doc in arr]
+    # assert all(len(x) == 4 for x in _array)
+    assert all(isinstance(x, list) for x in _array)
+    assert all(isinstance(x[0], str) for x in _array)
+
+
+def test_doc_to_target(task_class, limit):
+    arr = (
+        list(islice(task_class().test_docs(), limit))
+        if limit
+        else list(task_class().test_target())
+    )
+    _array_target = [task_class().doc_to_target(doc) for doc in arr]
+    assert all(isinstance(label, int) for label in _array_target)
+    assert len(_array_target) == limit if limit else True
+    # _array_text = [task.doc_to_text(doc) for doc in arr]
+    # Not working
+    # assert all(tgt[0] == " " or txt[-1] == "\n" if  len(txt) != 0 else True for txt, tgt in zip(_array_text, _array_target))
+
+
+def test_build_all_requests(task_class, limit):
+    task_class().build_all_requests(rank=1, limit=limit, world_size=1)
+    assert task_class.instances is not None
+
+
+def test_construct_requests(task_class, limit):
+    arr = (
+        list(islice(task_class().test_docs(), limit))
+        if limit
+        else list(task_class().test_docs())
+    )
+    requests = [
+        task_class().construct_requests(doc, task_class().doc_to_text(doc))
+        for doc in arr
+    ]
+    assert all(isinstance(doc, list) for doc in requests)
+    assert len(requests) == limit if limit else True
+
+
+# def test_create_choices(task_class):
+#     arr = list(islice(task_class().test_docs(), 1))
+#     choices = task_class().create_choices(arr[0])
+#     assert choices is not None
+# checking if number of choices is correct
+
+
+# @pytest.mark.parametrize("taskname,task_class", tasks.TASK_REGISTRY.items())
+# def test_basic_interface(taskname, task_class):
+#     print("Evaluating task", taskname)
+#     task = task_class()
+#
+#     assert task.has_training_docs() in [True, False]
+#     assert task.has_validation_docs() in [True, False]
+#     assert task.has_test_docs() in [True, False]
+#
+#     assert isinstance(task.aggregation(), dict)
+#     assert isinstance(task.higher_is_better(), dict)
+#     assert task.aggregation().keys() == task.higher_is_better().keys()
+#
+#     for v in task.higher_is_better().values():
+#         assert v in [True, False]
+#
+#     assert isinstance(task.VERSION, int)
+#
+#     # test deterministic docs
+#     # (don't test train because it's slow)
+#
+#     task2 = task_class()
+#
+#     limit = None
+#
+#     if taskname in ["triviaqa"] or taskname.startswith("pile_"):
+#         limit = 10000
+#     if task.has_validation_docs():
+#         arr = list(islice(task.validation_docs(), limit))
+#         arr2 = list(islice(task2.validation_docs(), limit))
+#
+#         assert arr == arr2
+#
+#         reqs = [task.construct_requests(doc, task.doc_to_text(doc)) for doc in arr]
+#         reqs2 = [task2.construct_requests(doc, task2.doc_to_text(doc)) for doc in arr2]
+#
+#         assert reqs == reqs2
+#
+#     if task.has_test_docs():
+#         arr = list(islice(task.test_docs(), limit))
+#         arr2 = list(islice(task2.test_docs(), limit))
+#
+#         assert arr == arr2
+#
+#         reqs = [task.construct_requests(doc, task.doc_to_text(doc)) for doc in arr]
+#         reqs2 = [task2.construct_requests(doc, task2.doc_to_text(doc)) for doc in arr2]
+#
+#         assert reqs == reqs2
+#
+#     if task.has_training_docs():
+#         arr = list(islice(task.training_docs(), limit))
+#         arr2 = list(islice(task2.training_docs(), limit))
+#
+#         assert arr == arr2
+#
+#         reqs = [task.construct_requests(doc, task.doc_to_text(doc)) for doc in arr]
+#         reqs2 = [task2.construct_requests(doc, task2.doc_to_text(doc)) for doc in arr2]
+#
+#         assert reqs == reqs2
+#
+#
+# @pytest.mark.parametrize("taskname,task_class", tasks.TASK_REGISTRY.items())
+# def test_documents_and_requests(taskname, task_class):
+#     print("Evaluating task", taskname)
+#     task = task_class()
+#     fns = []
+#     if task.has_training_docs():
+#         fns.append(task.training_docs)
+#     if task.has_validation_docs():
+#         fns.append(task.validation_docs)
+#     # test doc might not have labels
+#     # if task.has_test_docs(): fns.append(task.test_docs)
+#
+#     for fn in fns:
+#         # print(list(islice(fn(), 10)))
+#         for doc in islice(fn(), 10):
+#
+#             txt = task.doc_to_text(doc)
+#             tgt = task.doc_to_target(doc)
+#
+#             assert isinstance(txt, str)
+#             assert isinstance(tgt, str)
+#
+#             # space convention
+#             # allow txt to have length 0 for perplexity-like tasks since the model tacks an <|endoftext|> on
+#             if len(txt) != 0:
+#                 assert txt[-1] != " "
+#                 assert tgt[0] == " " or txt[-1] == "\n"
+#
+#             reqs = task.construct_requests(doc, txt)
+#
+#             # construct_requests can return just one request
+#             if not isinstance(reqs, (list, tuple)):
+#                 reqs = [reqs]
+#
+#             # todo: mock lm after refactoring evaluator.py to not be a mess
+#             # for req in reqs:
+#             #     assert isinstance(req, base.Request)

tests/test_description.py → tests/tests_master/test_description.py

@@ -6,7 +6,7 @@ import lm_eval.models
 def test_description():
     seed = 42
     num_examples = 1
-    task_names = ["arc_challenge", "lambada"]
+    task_names = ["arc_challenge", "arc_easy"]
     description_dict = {
         "arc_challenge": "Label for the relevant action:\nSentences describing context, with an incomplete sentence trailing answer that plausibly completes the situation.",
         "lambada": "Winograd schema sentence including a either a ___ blank with a missing word, making the pronoun ambiguous, or the same with the word filled in.",
@@ -40,6 +40,5 @@ def test_description():
             ctx = task.fewshot_context(
                 doc=doc,
                 num_fewshot=1,
-                rnd=rnd,
             )
             assert description in ctx

tests/test_generate_13_grams.py → tests/tests_master/test_generate_13_grams.py

@@ -44,9 +44,9 @@ def test_generate_13_grams_1(caplog):
         pass
     os.makedirs(test_working_directory)
 
-    assert not os.path.exists("pile")
-    os.makedirs("pile")
-    archive = Archive(os.path.join("pile", "test.jsonl.zst"))
+    assert not os.path.exists("../pile")
+    os.makedirs("../pile")
+    archive = Archive(os.path.join("../pile", "test.jsonl.zst"))
     archive.add_data(data)
     archive.commit()