base.py

import abc
import random
import numpy as np

from lm_eval.metrics import mean


class LM(abc.ABC):
    def __init__(self):
        self.cache_hook = CacheHook(None)

    @abc.abstractmethod
    def loglikelihood(self, requests):
        """Compute log-likelihood of generating a continuation from a context.
        Downstream tasks should attempt to use loglikelihood instead of other 
        LM calls whenever possible.

        :param requests: list
            A list of pairs (context, continuation)
            context: str
                Context string. Implementations of LM must be able to handle an 
                empty context string.
            continuation: str
                The continuation over which log likelihood will be calculated. If 
                there is a word boundary, the space should be in the continuation. 
                For example, context="hello" continuation=" world" is correct.
        :return: list
            A list of pairs (logprob, isgreedy)
            logprob: float
                The log probability of `continuation`
            isgreedy:
                Whether `continuation` would be generated by greedy sampling from `context`
        """
        pass

    @abc.abstractmethod
    def loglikelihood_perplexity(self, requests):
        """Compute full log-likelihood of a string, with no truncation, for perplexity computation
        - We will use the full max context length of the model.
        - For inputs that exceed the max context length, we divide the tokenized string into chunks of up to
        the max context length.
        - IMPORTANT: Each document's loglikelihood/perplexity is computed *separately*, unlike other implementaitons
          which may simply concatenate multiple documents together.
        - IMPORTANT: We maximize the amount of context for each prediction. Specifically, for inputs that we break into
          multiple chunks, the last input will still a full-sized context.
          Example:
            Input tokens: [ 0 1 2 3 4 5 6 7 8 9 ]
            Prefix: EOT
            Max context length: 4
            Resulting input/prediction pairs:

                INPUT:  EOT   0   1   2
                PRED:     0   1   2   3

                INPUT:    3   4   5   6
                PRED:     4   5   6   7

                INPUT:    5   6   7   8
                PRED:             8   9

          Observe that:
            1. Each token is predicted exactly once
            2. For the last pair, we provide the full context, but only score the last two tokens

        :param requests: list
            A list of strings
            string: str
                String for which we are computing per-toke  loglikelihood
        :return: list
            A list of pairs (logprob, isgreedy)
            logprob: float
                The log probability of `continuation`
            isgreedy:
                Whether `continuation` would be generated by greedy sampling from `context`
        """
        pass

    # TODO: Add an optional max length
    @abc.abstractmethod
    def greedy_until(self, requests):
        """Generate greedily until a stopping sequence

        :param requests: list
            A list of pairs (context, until)
            context: str
                Context string
            until: [str]
                The string sequences to generate until. These string sequences 
                may each span across multiple tokens, or may be part of one token.
        :return: list
            A list of strings continuation
            continuation: str
                The generated continuation.
        """
        pass

    @classmethod
    def create_from_arg_string(cls, arg_string):
        """Constructor method, in case models need additional arguments
        e.g. OpenAI API engine, paths for loading, other params

        :param arg_string: str
            Left up to individual model class to handle

        """
        return cls()

    def set_cache_hook(self, cache_hook):
        self.cache_hook = cache_hook


class Task(abc.ABC):
    """A task represents an entire benchmark including its dataset, problems,
    answers, and evaluation methods. See BoolQ for a simple example implementation

    A `doc` can be any python object which represents one instance of evaluation.
    This is usually a dictionary e.g.
        {"question": ..., "answer": ...} or
        {"question": ..., question, answer)
    """
    def __init__(self):
        self.download()
        self._training_docs = None
        self._fewshot_docs = None

    def download(self):
        """Downloads the task dataset if necessary"""
        pass

    @abc.abstractmethod
    def has_training_docs(self):
        """Whether the task has a training set"""
        pass

    @abc.abstractmethod
    def has_validation_docs(self):
        """Whether the task has a validation set"""
        pass

    @abc.abstractmethod
    def has_test_docs(self):
        """Whether the task has a test set"""
        pass

    def training_docs(self):
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def validation_docs(self):
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def test_docs(self):
        """
        :return: Iterable[obj]
            A iterable of any object, that doc_to_text can handle
        """
        return []

    def fewshot_examples(self, k, rnd):
        if self._training_docs is None:
            self._training_docs = list(self.training_docs())

        return rnd.sample(self._training_docs, k)

    @abc.abstractmethod
    def doc_to_text(self, doc):
        pass

    @abc.abstractmethod
    def doc_to_target(self, doc):
        pass

    @abc.abstractmethod
    def construct_requests(self, doc, ctx):
        """ Uses RequestFactory to construct Requests and returns an iterable of 
        Requests which will be sent to the LM.

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param ctx: str
            The context string, generated by fewshot_context. This includes the natural 
            language description, as well as the few shot examples, and the question
            part of the document for `doc`. 
        """
        pass

    @abc.abstractmethod
    def process_results(self, doc, results):
        """Take a single document and the LM results and evaluates, returning a 
        dict where keys are the names of submetrics and values are the values of 
        the metric for that one document

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :param results:
            The results of the requests created in construct_requests.
        """
        pass

    @abc.abstractmethod
    def aggregation(self):
        """
        :returns: {str: [metric_score] -> float}
            A dictionary where keys are the names of submetrics and values are 
            functions that aggregate a list of metric scores
        """
        pass

    @abc.abstractmethod
    def higher_is_better(self):
        """
        :returns: {str: bool}
            A dictionary where keys are the names of submetrics and values are 
            whether a higher value of the submetric is better
        """
        pass

    def fewshot_description(self):
        return ""

    def fewshot_context(self, doc, num_fewshot, provide_description, rnd):
        raw_description = self.fewshot_description()
        description = (raw_description + "\n===\n\n") if provide_description and raw_description else ""

        if num_fewshot == 0:
            labeled_examples = ""
        else:
            # for sets with no training docs, draw from other set *but ensure no overlap with current doc*
            if self.has_training_docs():
                fewshotex = self.fewshot_examples(k=num_fewshot, rnd=rnd)
            else:
                if self._fewshot_docs is None:
                    self._fewshot_docs = list(self.validation_docs() if self.has_validation_docs else self.test_docs())

                fewshotex = rnd.sample(self._fewshot_docs, num_fewshot + 1)

                # get rid of the doc that's the one we're evaluating, if it's in the fewshot
                fewshotex = [x for x in fewshotex if x != doc][:num_fewshot]

            labeled_examples = "\n\n".join(
                [self.doc_to_text(doc) + self.doc_to_target(doc) for doc in fewshotex]
            ) + "\n\n"

        example = self.doc_to_text(doc)
        return description + labeled_examples + example


class MultipleChoiceTask(Task):
    def doc_to_target(self, doc):
        return " " + doc['choices'][doc['gold']]

    def construct_requests(self, doc, ctx):
        lls = [
            rf.loglikelihood(ctx, " {}".format(choice))[0]
            for choice in doc['choices']
        ]

        return lls

    def process_results(self, doc, results):
        gold = doc["gold"]

        acc = 1. if np.argmax(results) == gold else 0.
        completion_len = np.array([float(len(i)) for i in doc["choices"]])
        acc_norm = 1. if np.argmax(results / completion_len) == gold else 0.

        return {
            "acc": acc,
            "acc_norm": acc_norm,
        }
    
    def higher_is_better(self):
        return {
            "acc": True,
            "acc_norm": True,
        }
    
    def aggregation(self):
        return {
            "acc": mean,
            "acc_norm": mean,
        }


class PerplexityTask(Task, abc.ABC):

    def has_training_docs(self):
        return False

    def fewshot_description(self):
        return ""

    def fewshot_examples(self, k, rnd):
        assert k == 0
        return []

    def fewshot_context(self, doc, num_fewshot, provide_description, rnd):
        assert num_fewshot == 0
        assert not provide_description
        return ""

    def higher_is_better(self):
        return False

    def doc_to_text(self, doc):
        return doc

    def doc_to_target(self, doc):
        raise NotImplementedError()
        return doc

    def construct_requests(self, doc, ctx):
        assert not ctx
        req = rf.loglikelihood_perplexity(doc)
        return req

    def process_results(self, doc, results):
        loglikelihood, = results
        return {
            "perplexity": loglikelihood,
        }

    def aggregation(self):
        return {
            "perplexity": self.compute_perplexity_from_loglikelihood,
        }

    @classmethod
    def compute_perplexity_from_loglikelihood(cls, loglikelihoods):
        aggregate_logprobs = np.concatenate(loglikelihoods)
        perplexity = np.exp(-aggregate_logprobs.mean())
        return float(perplexity)


req_ret_lens = {
    'loglikelihood': 2,
    'greedy_until': None,
    'loglikelihood_perplexity': 1,
}

import os
import json
import hashlib
from sqlitedict import SqliteDict

def hash_args(attr, args):
    dat = json.dumps([attr] + list(args))
    return hashlib.sha256(dat.encode('utf-8')).hexdigest()


class CacheHook:
    def __init__(self, cachinglm):
        if cachinglm is None: 
            self.dbdict = None
            return

        self.dbdict = cachinglm.dbdict
    
    def add_partial(self, attr, req, res):
        if self.dbdict is None:
            return
        hsh = hash_args(attr, req)
        self.dbdict[hsh] = res


class CachingLM:
    def __init__(self, lm, cache_db):
        self.lm = lm
        self.cache_db = cache_db
        os.makedirs(os.path.dirname(cache_db), exist_ok=True)
        self.dbdict = SqliteDict(cache_db, autocommit=True)

        # add hook to lm
        lm.set_cache_hook(self.get_cache_hook())

    def __getattr__(self, attr):
        def fn(requests):
            res = []
            remaining_reqs = []
            
            # figure out which ones are cached and which ones are new
            for req in requests:
                hsh = hash_args(attr, req)
                if hsh in self.dbdict:
                    ob = self.dbdict[hsh]

                    assert ob is not None

                    res.append(ob)
                else:
                    res.append(None)
                    remaining_reqs.append(req)
            
            # actually run the LM
            rem_res = getattr(self.lm, attr)(remaining_reqs)

            # stick the new ones back into the list and also cache any of the new ones
            resptr = 0
            for req, r in zip(remaining_reqs, rem_res):
                while res[resptr] is not None: resptr += 1

                res[resptr] = r

                # caching
                hsh = hash_args(attr, req)
                self.dbdict[hsh] = r
            self.dbdict.commit()

            return res
        return fn
    
    def get_cache_hook(self):
        return CacheHook(self)


class Request:
    def __init__(self, type, args, index=None):
        if type not in req_ret_lens.keys():
            raise NotImplementedError('The request type {} is not implemented!'.format(type))

        self.type = type
        self.args = args
        self.index = index
    
    def __iter__(self):
        if req_ret_lens[self.type] is None:
            raise IndexError('This request type does not return multiple arguments!')
        i = 0
        for i in range(req_ret_lens[self.type]):
            yield Request(self.type, self.args, i)
    
    def __getitem__(self, i):
        if req_ret_lens[self.type] is None:
            raise IndexError('This request type does not return multiple arguments!')
        return Request(self.type, self.args, i)
    
    def __eq__(self, other):
        return self.type == other.type and self.args == other.args and self.index == other.index

    def __repr__(self):
        return f"Req_{self.type}{self.args}[{self.index}]\n"

class RequestFactory:
    def __getattr__(self, attr):
        def fn(*args):
            return Request(attr, args)
        return fn


rf = RequestFactory()