evaluator.py

import collections
import itertools
import random

import lm_eval.metrics
import lm_eval.models
import lm_eval.tasks
import lm_eval.base
from lm_eval.utils import positional_deprecated, run_task_tests
from lm_eval.models.gpt2 import HFLM

import numpy as np
import transformers


@positional_deprecated
def simple_evaluate(
    model,
    model_args=None,
    tasks=[],
    num_fewshot=0,
    batch_size=None,
    max_batch_size=None,
    device=None,
    no_cache=False,
    limit=None,
    bootstrap_iters=100000,
    description_dict=None,
    check_integrity=False,
    decontamination_ngrams_path=None,
    write_out=False,
    output_base_path=None,
):
    """Instantiate and evaluate a model on a list of tasks.

    :param model: Union[str, LM]
        Name of model, transformers.PreTrainedModel object, or LM object, see lm_eval.models.get_model
    :param model_args: Optional[str]
        String arguments for each model class, see LM.create_from_arg_string.
        Ignored if `model` argument is a LM object.
    :param tasks: list[Union[str, Task]]
        List of task names or Task objects. Task objects will be taken to have name task.EVAL_HARNESS_NAME if defined and type(task).__name__ otherwise.
    :param num_fewshot: int
        Number of examples in few-shot context
    :param batch_size: int or str, optional
        Batch size for model
    :param max_batch_size: int, optional
        Maximal batch size to try with automatic batch size detection
    :param device: str, optional
        PyTorch device (e.g. "cpu" or "cuda:0") for running models
    :param no_cache: bool
        Whether or not to cache
    :param limit: int or float, optional
        Limit the number of examples per task (only use this for testing), If <1, limit is a percentage of the total number of examples.
    :param bootstrap_iters:
        Number of iterations for bootstrap statistics
    :param description_dict: dict[str, str]
        Dictionary of custom task descriptions of the form: `task_name: description`
    :param check_integrity: bool
        Whether to run the relevant part of the test suite for the tasks
    :param write_out: bool
        If True, write details about prompts and logits to json for all tasks
    :param output_base_path: str, optional
        Directory to which detailed eval info will be written. Defaults to present working dir.
    :return
        Dictionary of results
    """
    random.seed(1234)
    np.random.seed(1234)

    assert tasks != [], "No tasks specified"

    if isinstance(model, str):
        if model_args is None:
            model_args = ""
        lm = lm_eval.models.get_model(model).create_from_arg_string(
            model_args, {"batch_size": batch_size, "max_batch_size": max_batch_size, "device": device}
        )
    elif isinstance(model, transformers.PreTrainedModel):
        lm = lm_eval.models.get_model("hf-causal")(
                pretrained=model,
                batch_size=batch_size,
                max_batch_size=max_batch_size,
                )
        no_cache = True
    else:
        assert isinstance(model, lm_eval.base.LM)
        lm = model

    if not no_cache:
        lm = lm_eval.base.CachingLM(
            lm,
            "lm_cache/"
            + (model if isinstance(model, str) else model.model.config._name_or_path)
            + "_"
            + model_args.replace("=", "-").replace(",", "_").replace("/", "-")
            + ".db",
        )

    task_dict = lm_eval.tasks.get_task_dict(tasks)

    if check_integrity:
        run_task_tests(task_list=tasks)

    results = evaluate(
        lm=lm,
        task_dict=task_dict,
        num_fewshot=num_fewshot,
        limit=limit,
        bootstrap_iters=bootstrap_iters,
        description_dict=description_dict,
        decontamination_ngrams_path=decontamination_ngrams_path,
        write_out=write_out,
        output_base_path=output_base_path,
    )

    # add info about the model and few shot config
    model_name = None
    if isinstance(model, str):
        model_name = model
    elif isinstance(model, transformers.PreTrainedModel):
        model_name = "pretrained=" + model.config._name_or_path
    results["config"] = {
        "model": model_name,
        "model_args": model_args,
        "num_fewshot": num_fewshot,
        "batch_size": batch_size,
        "batch_sizes": list(lm.batch_sizes.values()) if hasattr(lm, "batch_sizes") else [],
        "device": device,
        "no_cache": no_cache,
        "limit": limit,
        "bootstrap_iters": bootstrap_iters,
        "description_dict": description_dict,
    }

    return results


decontaminate_suffix = "_decontaminate"


@positional_deprecated
def evaluate(
    lm,
    task_dict,
    provide_description=None,
    num_fewshot=0,
    limit=None,
    bootstrap_iters=100000,
    description_dict=None,
    decontamination_ngrams_path=None,
    write_out=False,
    output_base_path=None,
):
    """Instantiate and evaluate a model on a list of tasks.

    :param lm: obj
        Language Model
    :param task_dict: dict[str, Task]
        Dictionary of tasks. Tasks will be taken to have name task.EVAL_HARNESS_NAME if defined and type(task).__name__ otherwise.
    :param provide_description: bool
        Not implemented, and this option is deprecated and will be removed in a future version in favor of a different description providing method
    :param num_fewshot: int
        Number of examples in few-shot context
    :param limit: int, optional
        Limit the number of examples per task (only use this for testing)
    :param bootstrap_iters:
        Number of iterations for bootstrap statistics
    :param description_dict: dict[str, str]
        Dictionary of custom task descriptions of the form: `task_name: description`
    :param write_out: bool
        If True, write all prompts, logits and metrics to json for offline analysis
    :param output_base_path: str, optional
        Directory to which detailed eval info will be written. Defaults to present working dir
    :return
        Dictionary of results
    """
    # TODO: completely refactor this entire function to not be a huge mess, ideally breaking it down into smaller pieces

    # TODO: todo: implement proper description-providing system
    assert not provide_description  # not implemented.
    if provide_description is not None:
        # nudge people to not specify it at all
        print(
            "WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
        )

    decontaminate = decontamination_ngrams_path is not None

    task_dict_items = [
        (name, task)
        for name, task in task_dict.items()
        if (task.has_validation_docs() or task.has_test_docs())
    ]

    results = collections.defaultdict(dict)
    versions = collections.defaultdict(dict)

    requests = collections.defaultdict(list)
    requests_origin = collections.defaultdict(list)

    overlaps = collections.defaultdict(list)  # {task_name: contaminated_docs}

    # If we ever run into issues where the eval tasks don't fit in memory and we can't afford a machine with bigger
    # memory, we can always modify this plumbing to support that, but I didn't want to include it just yet because
    # over-engineering is bad (or we could make it write the requests to disk and then read them back out again
    #  - probably using an sqlite db because of all the moving parts we have

    # TODO: we need unit tests & sanity checks or something to ensure that the return of `validation_docs` is stable
    docs = {}
    write_out_info = {}

    docs_for_decontamination = collections.defaultdict(list)

    # get lists of each type of request
    for task_name, task in task_dict_items:
        versions[task_name] = task.VERSION
        # default to test doc, fall back to val doc if validation unavailable
        # TODO: the test-fallback-to-val system isn't final, we should revisit it at some point
        if task.has_test_docs():
            task_doc_func = task.test_docs
            task_set = "test"  # Required for caching in the decontamination
        elif task.has_validation_docs():
            task_set = "val"  # Required for caching in the decontamination
            task_doc_func = task.validation_docs
        else:
            raise RuntimeError("Task has neither test_docs nor validation_docs")

        # deterministically shuffle docs and chop off the first `limit` because sometimes docs are in some kind of order
        task_docs = list(task_doc_func())
        rnd = random.Random()
        rnd.seed(42)
        rnd.shuffle(task_docs)
        print(f"Task: {task_name}; number of docs: {len(task_docs)}")

        if write_out:
            prompt_details = []

        description = (
            description_dict[task_name]
            if description_dict and task_name in description_dict
            else ""
        )
        if limit is not None:
            limit = int(len(task_docs) * limit) if limit < 1.0 else int(limit)

        for doc_id, doc in enumerate(itertools.islice(task_docs, 0, limit)):
            if decontaminate and task.should_decontaminate():
                docs_for_decontamination[(task_name, task_set)].append(
                    task.doc_to_decontamination_query(doc)
                )

            docs[(task_name, doc_id)] = doc
            ctx = task.fewshot_context(
                doc=doc, num_fewshot=num_fewshot, rnd=rnd, description=description
            )
            reqs = task.construct_requests(doc, ctx)

            if write_out:
                prompt_details.append({"doc_id": doc_id})

            # print the prompt for the first few documents
            if doc_id < 1:
                print(
                    f"Task: {task_name}; document {doc_id}; context prompt (starting on next line):\n{ctx}\n(end of prompt on previous line)"
                )
                print("Requests:", reqs)

            if not isinstance(reqs, (list, tuple)):
                reqs = [reqs]
            for i, req in enumerate(reqs):
                requests[req.request_type].append(req)
                # i: index in requests for a single task instance
                # doc_id: unique id that we can get back to a doc using `docs`
                requests_origin[req.request_type].append((i, task_name, doc, doc_id))

                if write_out:
                    prompt_details[-1][f"prompt_{i}"] = "".join(
                        (map(lambda x: "".join(x), req.args))
                    )

        if write_out:
            write_out_info[task_name] = prompt_details

    # Compare all tasks/sets at once to ensure a single training set scan
    if decontaminate:
        from lm_eval.decontamination.decontaminate import get_train_overlap

        print("Finding train/test overlap, please wait...")
        overlaps = get_train_overlap(
            docs_for_decontamination, decontamination_ngrams_path, limit
        )

    # all responses for each (task, doc)
    process_res_queue = collections.defaultdict(list)

    # execute each type of request
    for reqtype, reqs in requests.items():
        # TODO: right now, this code runs multiple separate LM requests for multiple Requests differing
        #       only in index. We could implement some kind of caching, but that would be more of a band-aid
        #       solution. we could also implement some kind of auto-grouping here;
        #       they should end up next to each other.

        print("Running", reqtype, "requests")
        resps = getattr(lm, reqtype)([req.args for req in reqs])
        resps = [
            x if req.index is None else x[req.index] for x, req in zip(resps, reqs)
        ]

        for resp, (i, task_name, doc, doc_id) in zip(resps, requests_origin[reqtype]):
            process_res_queue[(task_name, doc_id)].append((i, resp))

            if write_out:
                write_out_info[task_name][doc_id][f"logit_{i}"] = resp
                task = task_dict[task_name]
                if isinstance(task, lm_eval.base.MultipleChoiceTask):
                    write_out_info[task_name][doc_id]["truth"] = doc["gold"]
                elif isinstance(task, lm_eval.tasks.winogrande.Winogrande):
                    write_out_info[task_name][doc_id]["truth"] = task.answer_to_num[
                        doc["answer"]
                    ]
                else:
                    write_out_info[task_name][doc_id]["truth"] = task.doc_to_target(doc)

    vals = collections.defaultdict(list)

    # unpack results and sort back in order and return control to Task
    for (task_name, doc_id), requests in process_res_queue.items():
        requests.sort(key=lambda x: x[0])
        requests = [x[1] for x in requests]

        task = task_dict[task_name]
        doc = docs[(task_name, doc_id)]

        metrics = task.process_results(doc, requests)
        for metric, value in metrics.items():
            vals[(task_name, metric)].append(value)

            if write_out:
                write_out_info[task_name][doc_id][metric] = str(value)

            # Re-use the evaluation for the decontaminated set by just ignoring the overlaps
            if decontaminate and task_name in overlaps:
                if doc_id not in overlaps[task_name]:
                    vals[(task_name, metric + decontaminate_suffix)].append(value)

    # aggregate results
    for (task_name, metric), items in vals.items():
        task = task_dict[task_name]
        real_metric = metric  # key when looking up the metric with task.aggregation
        if metric.endswith(decontaminate_suffix):
            real_metric = metric.replace(
                decontaminate_suffix, ""
            )  # decontaminated still uses the same metric
        results[task_name][metric] = task.aggregation()[real_metric](items)

        # hotfix: bleu, chrf, ter seem to be really expensive to bootstrap
        # so we run them less iterations. still looking for a cleaner way to do this

        stderr = lm_eval.metrics.stderr_for_metric(
            metric=task.aggregation()[real_metric],
            bootstrap_iters=min(bootstrap_iters, 1000)
            if metric in ["bleu", "chrf", "ter"]
            else bootstrap_iters,
        )

        if stderr is not None:
            results[task_name][metric + "_stderr"] = stderr(items)

    if write_out:
        import json
        import pathlib

        output_base_path = (
            pathlib.Path(output_base_path)
            if output_base_path is not None
            else pathlib.Path(".")
        )
        try:
            output_base_path.mkdir(parents=True, exist_ok=False)
        except FileExistsError:
            pass

        for task_name, _ in task_dict_items:
            with open(
                output_base_path.joinpath(f"{task_name}_write_out_info.json"),
                "w",
                encoding="utf8",
            ) as fp:
                json.dump(write_out_info[task_name], fp, indent=4, ensure_ascii=False)

    return {"results": dict(results), "versions": dict(versions)}


def make_table(result_dict):
    """Generate table of results."""
    from pytablewriter import MarkdownTableWriter, LatexTableWriter

    md_writer = MarkdownTableWriter()
    latex_writer = LatexTableWriter()
    md_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]
    latex_writer.headers = ["Task", "Version", "Metric", "Value", "", "Stderr"]

    values = []

    for k, dic in result_dict["results"].items():
        version = result_dict["versions"][k]
        for m, v in dic.items():
            if m.endswith("_stderr"):
                continue

            if m + "_stderr" in dic:
                se = dic[m + "_stderr"]
                values.append([k, version, m, "%.4f" % v, "±", "%.4f" % se])
            else:
                values.append([k, version, m, "%.4f" % v, "", ""])
            k = ""
            version = ""
    md_writer.value_matrix = values
    latex_writer.value_matrix = values

    # todo: make latex table look good
    # print(latex_writer.dumps())

    return md_writer.dumps()