evaluator.py 9.58 KB
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import collections
import itertools
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import random
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import lm_eval.metrics
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import lm_eval.models
import lm_eval.tasks
import lm_eval.base
import numpy as np
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from lm_eval.utils import positional_deprecated
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@positional_deprecated
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def simple_evaluate(model, model_args=None, tasks=[],
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                    num_fewshot=0, batch_size=None, device=None,
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                    no_cache=False, limit=None, bootstrap_iters=100000,
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                    description_dict=None):
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    """Instantiate and evaluate a model on a list of tasks.
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    :param model: Union[str, LM]
        Name of model 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
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    :param num_fewshot: int
        Number of examples in few-shot context
    :param batch_size: int, optional
        Batch size for model
    :param device: str, optional
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        PyTorch device (e.g. "cpu" or "cuda:0") for running models
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    :param no_cache: bool
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        Whether or not to cache
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    :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
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    :param description_dict: dict[str, str]
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        Dictionary of custom task descriptions of the form: `task_name: description` 
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    :return
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        Dictionary of results
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    """
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    random.seed(1234)
    np.random.seed(1234)

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    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, 'device': device
        })
    else:
        assert isinstance(model, lm_eval.base.LM)
        lm = model
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    if not no_cache:
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        lm = lm_eval.base.CachingLM(
            lm, 'lm_cache/' + model + '_' + model_args.replace('=', '-').replace(',', '_').replace('/', '-') + '.db'
        )
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    task_dict = lm_eval.tasks.get_task_dict(tasks)
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    results = evaluate(
        lm=lm,
        task_dict=task_dict,
        provide_description=False,
        num_fewshot=num_fewshot,
        limit=limit,
        description_dict=description_dict
    )
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    # add info about the model and few shot config
    results["config"] = {
        "model": model,
        "model_args": model_args,
        "num_fewshot": num_fewshot,
        "batch_size": batch_size,
        "device": device,
        "no_cache": no_cache,
        "limit": limit,
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        "bootstrap_iters": bootstrap_iters,
        "description_dict": description_dict
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    }

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

    :param lm: obj
        Language Model
    :param task_dict: dict[str, Task]
        Dictionary of tasks
    :param provide_description: bool
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        Not implemented, and this option is deprecated and will be removed in a future version in favor of a different description providing method
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    :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
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    :param description_dict: dict[str, str]
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        Dictionary of custom task descriptions of the form: `task_name: description` 
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    :return
        Dictionary of results
    """
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    # TODO: completely refactor this entire function to not be a huge mess, ideally breaking it down into smaller pieces

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    # TODO: todo: implement proper description-providing system
    assert not provide_description  # not implemented.

    task_dict_items = [
        (name, task)
        for name, task in task_dict.items()
        if(task.has_validation_docs() or task.has_test_docs())
    ]
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    results = collections.defaultdict(dict)
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    versions = collections.defaultdict(dict)
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    requests = collections.defaultdict(list)
    requests_origin = collections.defaultdict(list)

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

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

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        for doc_id, doc in enumerate(itertools.islice(task_docs, 0, limit)):
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            docs[(task_name, doc_id)] = doc
            ctx = task.fewshot_context(
                doc=doc,
                num_fewshot=num_fewshot,
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                provide_description=provide_description,
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                rnd=rnd,
                description=description
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            )
            reqs = task.construct_requests(doc, ctx)
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            if not isinstance(reqs, (list, tuple)):
                reqs = [reqs]
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            for i, req in enumerate(reqs):
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                requests[req.request_type].append(req)
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                # i: index in requests for a single task instance
                # doc_id: unique id that we can get back to a doc using `docs`
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                requests_origin[req.request_type].append((i, task_name, doc, doc_id))
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    # all responses for each (task, doc)
    process_res_queue = collections.defaultdict(list)

    # execute each type of request
    for reqtype, reqs in requests.items():
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        # 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.
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        print("Running", reqtype, "requests")
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        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))
    
    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)
    
    # aggregate results
    for (task_name, metric), items in vals.items():
        task = task_dict[task_name]
        results[task_name][metric] = task.aggregation()[metric](items)
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        # 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
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        stderr = lm_eval.metrics.stderr_for_metric(
            metric=task.aggregation()[metric],
            bootstrap_iters=min(bootstrap_iters, 1000) if metric in ["bleu", "chrf", "ter"] else bootstrap_iters,
        )
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        if stderr is not None:
            results[task_name][metric + "_stderr"] = stderr(items)
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    return {
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        "results": dict(results),
        "versions": dict(versions)
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    }
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def make_table(result_dict):
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    """Generate table of results."""
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    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():
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            if m.endswith("_stderr"):
                continue
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            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())

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    return md_writer.dumps()