superglue.py

"""
To-do:
    - WSC requires free-form generation
    - ReCoRD
"""
import numpy as np
import sklearn
import transformers.data.metrics.squad_metrics as squad_metrics
from . common import HFTask, yesno
from lm_eval.base import rf
from ..metrics import mean, acc_all, metric_max_over_ground_truths
from ..utils import general_detokenize


class BoolQ(HFTask):
    VERSION = 1
    DATASET_PATH = "super_glue"
    DATASET_NAME = "boolq"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

    def doc_to_text(self, doc):
        return f"{doc['passage']}\nQuestion: {doc['question']}?\nAnswer:"
    
    def doc_to_target(self, doc):
        return " " + yesno(doc['label']) 

    def construct_requests(self, doc, ctx):

        ll_yes, _ = rf.loglikelihood(ctx, ' yes')
        ll_no, _ = rf.loglikelihood(ctx, ' no')

        return ll_yes, ll_no

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

        acc = 1. if (ll_yes > ll_no) == gold else 0.

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


class CommitmentBank(HFTask):
    VERSION = 0
    DATASET_PATH = "super_glue"
    DATASET_NAME = "cb"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

    def doc_to_text(self, doc):
        return "{}\nQuestion: {}. True, False or Neither?\nAnswer:".format(
            doc["premise"],
            doc["hypothesis"],
        )

    def doc_to_target(self, doc):
        # True = entailment
        # False = contradiction
        # Neither = neutral
        return " {}".format({0: "True", 1: "Neither", 2: "False"}[doc["label"]])

    def construct_requests(self, doc, ctx):
        ll_true, _ = rf.loglikelihood(ctx, ' True')
        ll_neither, _ = rf.loglikelihood(ctx, ' Neither')
        ll_false, _ = rf.loglikelihood(ctx, ' False')

        return ll_true, ll_neither, ll_false

    def process_results(self, doc, results):
        gold = doc["label"]
        pred = np.argmax(results)
        acc = 1. if pred == gold else 0.

        return {
            "acc": acc,
            "f1": (pred, gold)
        }
    
    def higher_is_better(self):
        return {
            "acc": True,
            "f1": True
        }

    @classmethod
    def cb_multi_fi(cls, items):
        preds, golds = zip(*items)
        preds = np.array(preds)
        golds = np.array(golds)
        f11 = sklearn.metrics.f1_score(y_true=golds == 0, y_pred=preds == 0)
        f12 = sklearn.metrics.f1_score(y_true=golds == 1, y_pred=preds == 1)
        f13 = sklearn.metrics.f1_score(y_true=golds == 2, y_pred=preds == 2)
        avg_f1 = mean([f11, f12, f13])
        return avg_f1
    
    def aggregation(self):
        return {
            "acc": mean,
            "f1": self.cb_multi_fi,
        }


class Copa(HFTask):
    VERSION = 0
    DATASET_PATH = "super_glue"
    DATASET_NAME = "copa"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

    def doc_to_text(self, doc):
        # Drop the period
        connector = {
            "cause": "because",
            "effect": "therefore",
        }[doc["question"]]
        return doc["premise"].strip()[:-1] + f" {connector}"

    def doc_to_target(self, doc):
        correct_choice = doc["choice1"] if doc["label"] == 0 else doc["choice2"]
        # Connect the sentences
        return " " + self.convert_choice(correct_choice)

    def construct_requests(self, doc, ctx):
        choice1 = " " + self.convert_choice(doc["choice1"])
        choice2 = " " + self.convert_choice(doc["choice2"])
        
        ll_choice1, _ = rf.loglikelihood(ctx, choice1)
        ll_choice2, _ = rf.loglikelihood(ctx, choice2)

        return ll_choice1, ll_choice2

    def process_results(self, doc, results):
        gold = doc["label"]
        pred = np.argmax(results)
        acc = 1. if pred == gold else 0.

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

    @staticmethod
    def convert_choice(choice):
        return choice[0].lower() + choice[1:]


class MultiRC(HFTask):
    VERSION = 0
    DATASET_PATH = "super_glue"
    DATASET_NAME = "multirc"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

    def doc_to_text(self, doc):
        return f"{doc['paragraph']}\nQuestion: {doc['question']}\nAnswer:"

    def doc_to_target(self, doc):
        return " " + self.format_answer(answer=doc["answer"], label=doc["label"])

    @staticmethod
    def format_answer(answer, label):
        label_str = "yes" if label else "no"
        return f"{label_str}, {answer}"

    def construct_requests(self, doc, ctx):
        true_choice = self.format_answer(answer=doc["answer"], label=True)
        false_choice = self.format_answer(answer=doc["answer"], label=False)
        
        ll_true_choice, _ = rf.loglikelihood(ctx, f' {true_choice}')
        ll_false_choice, _ = rf.loglikelihood(ctx, f' {false_choice}')

        return ll_true_choice, ll_false_choice

    def process_results(self, doc, results):
        pred = np.argmax(results)
        return {
            "acc": (pred, doc)
        }
    
    def higher_is_better(self):
        return {
            "acc": True
        }
    
    def aggregation(self):
        return {
            "acc": acc_all
        }


class ReCoRD(HFTask):
    VERSION = 0
    DATASET_PATH = "super_glue"
    DATASET_NAME = "record"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

    def training_docs(self):
        # In ReCoRD, each doc manifests multiple "examples" in the context of few shot example packing.
        # Each doc consists of multiple answer candidates, each of which is scored yes/no.
        if self._training_docs is None:
            self._training_docs = []
            for doc in self.data["train"]:
                self._training_docs.append(self._process_doc(doc))
        return self._training_docs

    def validation_docs(self):
        # See: training_docs
        for doc in self.data["validation"]:
            yield self._process_doc(doc)

    @classmethod
    def _process_doc(cls, doc):
        return {
            "passage": doc["passage"],
            "query": doc["query"],
            "entities": sorted(list(set(doc["entities"]))),
            "answers": sorted(list(set(doc["answers"]))),
        }

    def doc_to_text(self, doc):
        initial_text, *highlights = doc["passage"].strip().split("\n@highlight\n")
        text = initial_text + "\n\n"
        for highlight in highlights:
            text += f"  - {highlight}.\n"
        return text

    @classmethod
    def format_answer(cls, query, entity):
        return f'  - {query}'.replace("@placeholder", entity)

    def doc_to_target(self, doc):
        # We only output the first correct entity in a doc
        return self.format_answer(query=doc["query"], entity=doc["answers"][0])

    def construct_requests(self, doc, ctx):
        requests = [
            rf.loglikelihood(ctx, self.format_answer(query=doc["query"], entity=entity))
            for entity in doc["entities"]
        ]
        return requests

    def process_results(self, doc, results):
        # ReCoRD's evaluation is actually deceptively simple:
        # - Pick the maximum likelihood prediction entity
        # - Evaluate the accuracy and token F1 PER EXAMPLE
        # - Average over all examples
        max_idx = np.argmax(np.array([result[0] for result in results]))

        prediction = doc["entities"][max_idx]
        gold_label_set = doc["answers"]
        f1 = metric_max_over_ground_truths(squad_metrics.compute_f1, prediction, gold_label_set)
        em = metric_max_over_ground_truths(squad_metrics.compute_exact, prediction, gold_label_set)

        return {
            "f1": f1,
            "em": em,
        }

    def higher_is_better(self):
        return {
            "f1": True,
            "em": True,
        }

    def aggregation(self):
        return {
            "f1": mean,
            "em": mean,
        }


class WordsInContext(HFTask):
    VERSION = 0
    DATASET_PATH = "super_glue"
    DATASET_NAME = "wic"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

    def doc_to_text(self, doc):
        return "Sentence 1: {}\nSentence 2: {}\nQuestion: Is the word '{}' used in the same way in the" \
               " two sentences above?\nAnswer:".format(
                    doc["sentence1"],
                    doc["sentence2"],
                    doc["sentence1"][doc["start1"]:doc["end1"]],
                )

    def doc_to_target(self, doc):
        return " {}".format({0: "no", 1: "yes"}[doc["label"]])

    def construct_requests(self, doc, ctx):
        ll_yes, _ = rf.loglikelihood(ctx, ' yes')
        ll_no, _ = rf.loglikelihood(ctx, ' no')

        return ll_yes, ll_no

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

        acc = 1. if (ll_yes > ll_no) == gold else 0.

        return {
            "acc": acc
        }

    def higher_is_better(self):
        return {
            "acc": True
        }

    def aggregation(self):
        return {
            "acc": mean
        }


class SGWinogradSchemaChallenge(HFTask):
    VERSION = 0
    # Note: This implementation differs from Fig G.32 because this is the SuperGLUE,
    #       binary version of the task.
    DATASET_PATH = "super_glue"
    DATASET_NAME = "wsc"

    def has_training_docs(self):
        return True

    def has_validation_docs(self):
        return True

    def has_test_docs(self):
        return False

    def training_docs(self):
        if self.has_training_docs():
            if self._training_docs is None:
                # GPT-3 Paper's format only uses positive examples for fewshot "training"
                self._training_docs = [
                    doc for doc in
                    self.data["train"]
                    if doc["label"]
                ]
            return self._training_docs

    def doc_to_text(self, doc):
        raw_passage = doc["text"]
        # NOTE: HuggingFace span indices are word-based not character-based.
        pre = " ".join(raw_passage.split()[:doc["span2_index"]])
        post = raw_passage[len(pre) + len(doc["span2_text"]) + 1:]
        passage = general_detokenize(pre + " *{}*".format(doc['span2_text']) + post)
        noun = doc["span1_text"]
        pronoun = doc["span2_text"]
        text = (
            f"Passage: {passage}\n"
            + f"Question: In the passage above, does the pronoun \"*{pronoun}*\" refer to \"*{noun}*\"?\n"
            + "Answer:"
        )
        return text

    def doc_to_target(self, doc):
        return " " + yesno(doc['label'])

    def construct_requests(self, doc, ctx):

        ll_yes, _ = rf.loglikelihood(ctx, ' yes')
        ll_no, _ = rf.loglikelihood(ctx, ' no')

        return ll_yes, ll_no

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

        acc = 1. if (ll_yes > ll_no) == gold else 0.

        return {
            "acc": acc
        }

    def higher_is_better(self):
        return {
            "acc": True
        }

    def aggregation(self):
        return {
            "acc": mean
        }