diff --git a/packages/remotes/pyproject.toml b/packages/remotes/pyproject.toml index 74c1889..42cfcc7 100644 --- a/packages/remotes/pyproject.toml +++ b/packages/remotes/pyproject.toml @@ -42,7 +42,7 @@ requires-python = ">=3.11" dev = [ "ether0", "ether0.remotes[serve]", - "tensorboard>=2.19", # Indirect dependency we pin to keep recent + "tensorboard>=2.18", # Indirect dependency we pin to keep recent ] serve = ["uvicorn"] diff --git a/pyproject.toml b/pyproject.toml index dee4d4b..ae71e15 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -55,7 +55,7 @@ baselines = [ "ipython", ] dev = [ - "ether0[add-tokens,typing]", + "ether0[add-tokens,lighteval,typing]", "huggingface-hub[cli]", # For login inside of CI "ipython>=8", # Pin to keep recent "mypy>=1.8", # For addition of mutable-override @@ -69,6 +69,10 @@ dev = [ "refurb>=2", # Pin to keep recent "typeguard", ] +lighteval = [ + "aenum", + "lighteval[litellm]>=0.10", # Pin to keep recent +] typing = [ "types-regex", ] @@ -169,8 +173,10 @@ warn_unused_ignores = true ignore_missing_imports = true # Per-module configuration options module = [ + "aenum", # SEE: https://github.com/ethanfurman/aenum/issues/10 "datasets.*", # SEE: https://github.com/huggingface/datasets/issues/3841 "huggingface_hub.*", # SEE: https://github.com/huggingface/huggingface_hub/issues/1662 + "lighteval.*", # SEE: https://github.com/huggingface/lighteval/issues/749 "molbloom", # SEE: https://github.com/whitead/molbloom/issues/29 "molsol", # SEE: https://github.com/maykcaldas/molsol/issues/6 "onmt.*", diff --git a/src/ether0/lighteval_tasks.py b/src/ether0/lighteval_tasks.py new file mode 100644 index 0000000..ae4c008 --- /dev/null +++ b/src/ether0/lighteval_tasks.py @@ -0,0 +1,182 @@ +import logging +import statistics +from collections.abc import Collection, Iterable +from typing import Any + +from datasets import load_dataset + +try: + from aenum import extend_enum + from lighteval.metrics.metrics import ( + MetricCategory, + Metrics, + MetricUseCase, + SampleLevelMetric, + ) + from lighteval.tasks.lighteval_task import LightevalTaskConfig + from lighteval.tasks.requests import Doc +except ImportError as exc: + raise ImportError( + "To use ether0's LightEval tasks, please install the 'lighteval' extra via:" + " `pip install ether0[lighteval]`." + ) from exc + +from ether0.data import get_problem_category +from ether0.model_prompts import LOOSE_XML_ANSWER_USER_PROMPT, ProblemPrompt +from ether0.models import make_problem_types_filter +from ether0.rewards import accuracy_reward, format_reward + +logger = logging.getLogger(__name__) + +ETHER0_ACCURACY_METRIC_NAME = "ether0_accuracy" +ETHER0_FORMAT_METRIC_NAME = "ether0_format" + + +def evaluate_ether0_accuracy( + predictions: list[str], + formatted_doc: Doc, + golds: list[str] | None = None, # noqa: ARG001 +) -> float: + if len(predictions) != 1: + raise NotImplementedError( + "Didn't handle anything besides one prediction" + f" for doc {formatted_doc}, got {predictions}." + ) + return accuracy_reward( + completions=predictions, + solution=[formatted_doc.specific["solution"]], + reasoning=formatted_doc.specific["reasoning"], + soft=formatted_doc.specific["soft"], + test=formatted_doc.specific["test"], + )[0] + + +def evaluate_ether0_format( + predictions: list[str], + formatted_doc: Doc, + golds: list[str] | None = None, # noqa: ARG001 +) -> float: + if len(predictions) != 1: + raise NotImplementedError( + "Didn't handle anything besides one prediction" + f" for doc {formatted_doc}, got {predictions}." + ) + if formatted_doc.specific["test"]: + logger.warning("ether0's format reward is only applicable at training time.") + return format_reward( + completions=predictions, + reasoning=formatted_doc.specific["reasoning"], + )[0] + + +for metric_name, metric_eval_fn in ( + (ETHER0_ACCURACY_METRIC_NAME, evaluate_ether0_accuracy), + (ETHER0_FORMAT_METRIC_NAME, evaluate_ether0_format), +): + if ( # Work around https://github.com/huggingface/lighteval/issues/805 + metric_name not in Metrics.__members__ + ): + extend_enum( + Metrics, + metric_name, + SampleLevelMetric( + metric_name=metric_name, + higher_is_better=True, + category=MetricCategory.GENERATIVE, + use_case=MetricUseCase.ACCURACY, + sample_level_fn=metric_eval_fn, + corpus_level_fn=statistics.mean, + ), + ) + + +KEYS_TO_STORE_IN_DOC = {"id", "solution"} + + +def make_ether0_task( + name: str, + soft: bool, + test: bool, + reasoning: bool, + problem_types: str | Collection[str] | None = None, + metric_names: Iterable[str] | None = None, + **kwargs, +) -> LightevalTaskConfig: + """Create LightEval task for the ether0-benchmark dataset.""" + reward_fn_kwargs = {"soft": soft, "test": test, "reasoning": reasoning} + if not test: + prob_prompt = ProblemPrompt.THINK_ANSWER if reasoning else ProblemPrompt.ANSWER + prompt_prefix: str = prob_prompt.get_prompt() + else: + prompt_prefix = LOOSE_XML_ANSWER_USER_PROMPT + + def row_to_doc(row: dict[str, Any], task_name: str) -> Doc: + """Convert an ether0-benchmark dataset row to a LightEval Doc.""" + return Doc( + query="\n\n".join((prompt_prefix, row["problem"])), + task_name=task_name, + choices=[""], # Placeholder for non-QA tasks + gold_index=0, # Points to above placeholder + specific={k: row[k] for k in KEYS_TO_STORE_IN_DOC} | reward_fn_kwargs, + ) + + if metric_names is None: + metric_names = ( + (ETHER0_ACCURACY_METRIC_NAME, ETHER0_FORMAT_METRIC_NAME) + if not test + else (ETHER0_ACCURACY_METRIC_NAME,) + ) + return LightevalTaskConfig( + name=name, + prompt_function=row_to_doc, + suite=["community"], + hf_repo="futurehouse/ether0-benchmark", + hf_subset="default", + hf_filter=( + make_problem_types_filter(problem_types, type_col="problem_type") + if problem_types is not None + else None + ), + hf_avail_splits=["test"], + evaluation_splits=["test"], + metric=[getattr(Metrics, metric_name) for metric_name in metric_names], + **kwargs, + ) + + +# TASKS_TABLE is required by LightEval for --custom-tasks CLI arg +TASKS_TABLE = [ # Add general tasks + make_ether0_task( + f"ether0:{nickname}{':soft' if is_soft else ''}", + soft=is_soft, + test=kwargs["test"], + reasoning=kwargs["reasoning"], + ) + for is_soft in (False, True) + for nickname, kwargs in ( + ("loose", {"test": True, "reasoning": False}), + ("strict:no_reasoning", {"test": False, "reasoning": False}), + ("strict", {"test": False, "reasoning": True}), + ) +] +TASKS_TABLE.extend([ # Add problem type-specific tasks + make_ether0_task( + f"ether0:{nickname}{':soft' if is_soft else ''}:{prob_cat}", + soft=is_soft, + test=kwargs["test"], + reasoning=kwargs["reasoning"], + problem_types=f"re:^{prob_cat}.*$", + ) + for is_soft in (False, True) + for nickname, kwargs in ( + ("loose", {"test": True, "reasoning": False}), + ("strict:no_reasoning", {"test": False, "reasoning": False}), + ("strict", {"test": False, "reasoning": True}), + ) + for prob_cat in { + get_problem_category(pt) + for pt in load_dataset("futurehouse/ether0-benchmark", split="test")[ + "problem_type" + ] + } +]) diff --git a/src/ether0/models.py b/src/ether0/models.py index ce39fe4..ad0bfde 100644 --- a/src/ether0/models.py +++ b/src/ether0/models.py @@ -1,5 +1,5 @@ import re -from collections.abc import Collection +from collections.abc import Callable, Collection, Mapping from enum import StrEnum, auto from typing import Any @@ -104,16 +104,14 @@ class QAExample(BaseModel): ) -def filter_problem_types( - dataset: TDataset, problem_types: str | Collection[str] | None -) -> TDataset: - """Filter a dataset by problem types. +def make_problem_types_filter( + problem_types: str | Collection[str], type_col: str +) -> Callable[[Mapping[str, Any]], bool]: + """Make a filtration function to filter a dataset by problem types. Args: - dataset: The dataset to filter. Can be a single Dataset or a DatasetDict. problem_types: A string or collection of strings specifying the problem types to filter by. - - If None, the original dataset is returned. - If a string or a collection of strings: - Strings starting with "re:" are treated as regex patterns. If a regex filter is provided, then it must be the only filter. @@ -121,22 +119,15 @@ def filter_problem_types( - Other strings are treated as exact problem types to include. - Mixing inclusion and exclusion rules (e.g. ["type_a", "!type_b"]) is not allowed. + type_col: The column name in the dataset that contains the problem type. Returns: - The filtered dataset. + Callable that returns True to keep a row, otherwise False to filter it out. """ - if problem_types is None: - return dataset if isinstance(problem_types, str): # Assume single problem type as a string problem_types = [problem_types] problem_types = {pt.strip() for pt in problem_types} - columns = ( - next(iter(dataset.values())) if isinstance(dataset, DatasetDict) else dataset - ).column_names - # ether0-benchmark uses 'problem_type'; some variants may use 'type' - type_col = "problem_type" if "problem_type" in columns else "type" - if any(pt.startswith("re:") for pt in problem_types): # A regex was passed in if len(problem_types) != 1: @@ -170,4 +161,31 @@ def filter_func(x): def filter_func(x): return x[type_col] not in invalid_problem_types - return dataset.filter(filter_func, desc="Filtering problem types") + return filter_func + + +def filter_problem_types( + dataset: TDataset, problem_types: str | Collection[str] | None +) -> TDataset: + """Filter a dataset by problem types. + + Args: + dataset: The dataset to filter. Can be a single Dataset or a DatasetDict. + problem_types: See make_problem_types_filter.__doc__. + + Returns: + The filtered dataset. + """ + if problem_types is None: + return dataset + + columns = ( + next(iter(dataset.values())) if isinstance(dataset, DatasetDict) else dataset + ).column_names + # ether0-benchmark uses 'problem_type'; some variants may use 'type' + type_col = "problem_type" if "problem_type" in columns else "type" + + return dataset.filter( + make_problem_types_filter(problem_types, type_col), + desc="Filtering problem types", + ) diff --git a/tests/test_lighteval_tasks.py b/tests/test_lighteval_tasks.py new file mode 100644 index 0000000..0467298 --- /dev/null +++ b/tests/test_lighteval_tasks.py @@ -0,0 +1,80 @@ +from unittest.mock import patch + +from lighteval.main_tasks import list as lighteval_list +from lighteval.metrics.metrics import Metrics, SampleLevelMetric +from lighteval.tasks.requests import Doc + +import ether0.lighteval_tasks + + +def test_task_list(capsys) -> None: + """Integration test designed to test TASKS_TABLE and custom task creation.""" + with patch( # Work around https://github.com/huggingface/lighteval/issues/805 + "lighteval.tasks.registry.create_custom_tasks_module", + side_effect=[ether0.lighteval_tasks], + ): + lighteval_list(custom_tasks=ether0.lighteval_tasks.__file__) + captured = capsys.readouterr() + assert not captured.err + tasks = [row for row in captured.out.splitlines() if "ether0" in row] + assert len(tasks) > 1, "Expected some ether0 tasks" + assert any( + "functional-group" in row for row in tasks + ), "Expected specific tasks to be listed" + # TODO: after https://github.com/huggingface/lighteval/issues/806, + # remove the .litellm_cache directory created by this test importing from LightEval + + +def test_accuracy_metric() -> None: + accuracy_metric = getattr( + Metrics, ether0.lighteval_tasks.ETHER0_ACCURACY_METRIC_NAME + ).value + assert isinstance(accuracy_metric, SampleLevelMetric) + + # NOTE: these inputs were taken from a gpt-4o baseline run + doc_json = { + "query": ( + "When answering, be sure to place the final answer as SMILES notation into" + " XML tags . An example is CCO.\n\nWhat" + " is a valid completion of this molecule:\nO=C(OCC1=CC=CC=C1)N1CCCC1C(=O" + ), + "choices": [""], + "gold_index": 0, + "original_query": "", + "specific": { + "solution": ( + "valid_mol_eval!:!O=C(OCC1=CC=CC=C1)N1CCCC1C(=O!:!molecule-completion" + ), + "id": "e8b8bb34-731a-46e1-93a2-b6330a705148", + "soft": False, + "test": True, + "reasoning": False, + }, + "task_name": "community|ether0:loose:molecule-completion", + "instruction": "", + "ctx": [{ + "role": "user", + "content": ( + "When answering, be sure to place the final answer as SMILES notation" + " into XML tags . An example is" + " CCO.\n\nWhat is a valid completion of this" + " molecule:\nO=C(OCC1=CC=CC=C1)N1CCCC1C(=O" + ), + }], + "num_asked_few_shots": 0, + "num_effective_few_shots": 0, + } + assert ( + accuracy_metric.sample_level_fn( + predictions=[ + "The given fragment of the molecule O=C(OCC1=CC=CC=C1)N1CCCC1C(=O suggests" + " a structure that indicates an amide linkage with a substituted" + " cyclohexanone. A plausible completion of this structure is a standard" + " cyclohexanone amide. 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