utils_multiple_choice.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" Multiple choice fine-tuning: utilities to work with multiple choice tasks of reading comprehension """


import csv
import glob
import json
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional

import torch
import tqdm
from torch.utils.data.dataset import Dataset

from transformers import PreTrainedTokenizer, torch_distributed_zero_first


logger = logging.getLogger(__name__)


@dataclass(frozen=True)
class InputExample:
    """
    A single training/test example for multiple choice

    Args:
        example_id: Unique id for the example.
        question: string. The untokenized text of the second sequence (question).
        contexts: list of str. The untokenized text of the first sequence (context of corresponding question).
        endings: list of str. multiple choice's options. Its length must be equal to contexts' length.
        label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
    """

    example_id: str
    question: str
    contexts: List[str]
    endings: List[str]
    label: Optional[str]


@dataclass(frozen=True)
class InputFeatures:
    """
    A single set of features of data.
    Property names are the same names as the corresponding inputs to a model.
    """

    example_id: str
    input_ids: List[List[int]]
    attention_mask: Optional[List[List[int]]]
    token_type_ids: Optional[List[List[int]]]
    label: Optional[int]


class Split(Enum):
    train = "train"
    dev = "dev"
    test = "test"


class MultipleChoiceDataset(Dataset):
    """
    This will be superseded by a framework-agnostic approach
    soon.
    """

    features: List[InputFeatures]

    def __init__(
        self,
        data_dir: str,
        tokenizer: PreTrainedTokenizer,
        task: str,
        max_seq_length: Optional[int] = None,
        overwrite_cache=False,
        mode: Split = Split.train,
        local_rank=-1,
    ):
        processor = processors[task]()

        cached_features_file = os.path.join(
            data_dir,
            "cached_{}_{}_{}_{}".format(mode.value, tokenizer.__class__.__name__, str(max_seq_length), task,),
        )
        with torch_distributed_zero_first(local_rank):
            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.

            if os.path.exists(cached_features_file) and not overwrite_cache:
                logger.info(f"Loading features from cached file {cached_features_file}")
                self.features = torch.load(cached_features_file)
            else:
                logger.info(f"Creating features from dataset file at {data_dir}")
                label_list = processor.get_labels()
                if mode == Split.dev:
                    examples = processor.get_dev_examples(data_dir)
                elif mode == Split.test:
                    examples = processor.get_test_examples(data_dir)
                else:
                    examples = processor.get_train_examples(data_dir)
                logger.info("Training examples: %s", len(examples))
                # TODO clean up all this to leverage built-in features of tokenizers
                self.features = convert_examples_to_features(
                    examples,
                    label_list,
                    max_seq_length,
                    tokenizer,
                    pad_on_left=bool(tokenizer.padding_side == "left"),
                    pad_token=tokenizer.pad_token_id,
                    pad_token_segment_id=tokenizer.pad_token_type_id,
                )
                if local_rank in [-1, 0]:
                    logger.info("Saving features into cached file %s", cached_features_file)
                    torch.save(self.features, cached_features_file)

    def __len__(self):
        return len(self.features)

    def __getitem__(self, i) -> InputFeatures:
        return self.features[i]


class DataProcessor:
    """Base class for data converters for multiple choice data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_test_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the test set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()


class RaceProcessor(DataProcessor):
    """Processor for the RACE data set."""

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {} train".format(data_dir))
        high = os.path.join(data_dir, "train/high")
        middle = os.path.join(data_dir, "train/middle")
        high = self._read_txt(high)
        middle = self._read_txt(middle)
        return self._create_examples(high + middle, "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {} dev".format(data_dir))
        high = os.path.join(data_dir, "dev/high")
        middle = os.path.join(data_dir, "dev/middle")
        high = self._read_txt(high)
        middle = self._read_txt(middle)
        return self._create_examples(high + middle, "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {} test".format(data_dir))
        high = os.path.join(data_dir, "test/high")
        middle = os.path.join(data_dir, "test/middle")
        high = self._read_txt(high)
        middle = self._read_txt(middle)
        return self._create_examples(high + middle, "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1", "2", "3"]

    def _read_txt(self, input_dir):
        lines = []
        files = glob.glob(input_dir + "/*txt")
        for file in tqdm.tqdm(files, desc="read files"):
            with open(file, "r", encoding="utf-8") as fin:
                data_raw = json.load(fin)
                data_raw["race_id"] = file
                lines.append(data_raw)
        return lines

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (_, data_raw) in enumerate(lines):
            race_id = "%s-%s" % (set_type, data_raw["race_id"])
            article = data_raw["article"]
            for i in range(len(data_raw["answers"])):
                truth = str(ord(data_raw["answers"][i]) - ord("A"))
                question = data_raw["questions"][i]
                options = data_raw["options"][i]

                examples.append(
                    InputExample(
                        example_id=race_id,
                        question=question,
                        contexts=[article, article, article, article],  # this is not efficient but convenient
                        endings=[options[0], options[1], options[2], options[3]],
                        label=truth,
                    )
                )
        return examples


class SwagProcessor(DataProcessor):
    """Processor for the SWAG data set."""

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {} train".format(data_dir))
        return self._create_examples(self._read_csv(os.path.join(data_dir, "train.csv")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {} dev".format(data_dir))
        return self._create_examples(self._read_csv(os.path.join(data_dir, "val.csv")), "dev")

    def get_test_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {} dev".format(data_dir))
        raise ValueError(
            "For swag testing, the input file does not contain a label column. It can not be tested in current code"
            "setting!"
        )
        return self._create_examples(self._read_csv(os.path.join(data_dir, "test.csv")), "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1", "2", "3"]

    def _read_csv(self, input_file):
        with open(input_file, "r", encoding="utf-8") as f:
            return list(csv.reader(f))

    def _create_examples(self, lines: List[List[str]], type: str):
        """Creates examples for the training and dev sets."""
        if type == "train" and lines[0][-1] != "label":
            raise ValueError("For training, the input file must contain a label column.")

        examples = [
            InputExample(
                example_id=line[2],
                question=line[5],  # in the swag dataset, the
                # common beginning of each
                # choice is stored in "sent2".
                contexts=[line[4], line[4], line[4], line[4]],
                endings=[line[7], line[8], line[9], line[10]],
                label=line[11],
            )
            for line in lines[1:]  # we skip the line with the column names
        ]

        return examples


class ArcProcessor(DataProcessor):
    """Processor for the ARC data set (request from allennlp)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {} train".format(data_dir))
        return self._create_examples(self._read_json(os.path.join(data_dir, "train.jsonl")), "train")

    def get_dev_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {} dev".format(data_dir))
        return self._create_examples(self._read_json(os.path.join(data_dir, "dev.jsonl")), "dev")

    def get_test_examples(self, data_dir):
        logger.info("LOOKING AT {} test".format(data_dir))
        return self._create_examples(self._read_json(os.path.join(data_dir, "test.jsonl")), "test")

    def get_labels(self):
        """See base class."""
        return ["0", "1", "2", "3"]

    def _read_json(self, input_file):
        with open(input_file, "r", encoding="utf-8") as fin:
            lines = fin.readlines()
            return lines

    def _create_examples(self, lines, type):
        """Creates examples for the training and dev sets."""

        # There are two types of labels. They should be normalized
        def normalize(truth):
            if truth in "ABCD":
                return ord(truth) - ord("A")
            elif truth in "1234":
                return int(truth) - 1
            else:
                logger.info("truth ERROR! %s", str(truth))
                return None

        examples = []
        three_choice = 0
        four_choice = 0
        five_choice = 0
        other_choices = 0
        # we deleted example which has more than or less than four choices
        for line in tqdm.tqdm(lines, desc="read arc data"):
            data_raw = json.loads(line.strip("\n"))
            if len(data_raw["question"]["choices"]) == 3:
                three_choice += 1
                continue
            elif len(data_raw["question"]["choices"]) == 5:
                five_choice += 1
                continue
            elif len(data_raw["question"]["choices"]) != 4:
                other_choices += 1
                continue
            four_choice += 1
            truth = str(normalize(data_raw["answerKey"]))
            assert truth != "None"
            question_choices = data_raw["question"]
            question = question_choices["stem"]
            id = data_raw["id"]
            options = question_choices["choices"]
            if len(options) == 4:
                examples.append(
                    InputExample(
                        example_id=id,
                        question=question,
                        contexts=[
                            options[0]["para"].replace("_", ""),
                            options[1]["para"].replace("_", ""),
                            options[2]["para"].replace("_", ""),
                            options[3]["para"].replace("_", ""),
                        ],
                        endings=[options[0]["text"], options[1]["text"], options[2]["text"], options[3]["text"]],
                        label=truth,
                    )
                )

        if type == "train":
            assert len(examples) > 1
            assert examples[0].label is not None
        logger.info("len examples: %s}", str(len(examples)))
        logger.info("Three choices: %s", str(three_choice))
        logger.info("Five choices: %s", str(five_choice))
        logger.info("Other choices: %s", str(other_choices))
        logger.info("four choices: %s", str(four_choice))

        return examples


def convert_examples_to_features(
    examples: List[InputExample],
    label_list: List[str],
    max_length: int,
    tokenizer: PreTrainedTokenizer,
    pad_token_segment_id=0,
    pad_on_left=False,
    pad_token=0,
    mask_padding_with_zero=True,
) -> List[InputFeatures]:
    """
    Loads a data file into a list of `InputFeatures`
    """

    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in tqdm.tqdm(enumerate(examples), desc="convert examples to features"):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d of %d" % (ex_index, len(examples)))
        choices_inputs = []
        for ending_idx, (context, ending) in enumerate(zip(example.contexts, example.endings)):
            text_a = context
            if example.question.find("_") != -1:
                # this is for cloze question
                text_b = example.question.replace("_", ending)
            else:
                text_b = example.question + " " + ending

            inputs = tokenizer.encode_plus(
                text_a, text_b, add_special_tokens=True, max_length=max_length, pad_to_max_length=True,
            )
            if "num_truncated_tokens" in inputs and inputs["num_truncated_tokens"] > 0:
                logger.info(
                    "Attention! you are cropping tokens (swag task is ok). "
                    "If you are training ARC and RACE and you are poping question + options,"
                    "you need to try to use a bigger max seq length!"
                )

            choices_inputs.append(inputs)

        label = label_map[example.label]

        input_ids = [x["input_ids"] for x in choices_inputs]
        attention_mask = (
            [x["attention_mask"] for x in choices_inputs] if "attention_mask" in choices_inputs[0] else None
        )
        token_type_ids = (
            [x["token_type_ids"] for x in choices_inputs] if "token_type_ids" in choices_inputs[0] else None
        )

        features.append(
            InputFeatures(
                example_id=example.example_id,
                input_ids=input_ids,
                attention_mask=attention_mask,
                token_type_ids=token_type_ids,
                label=label,
            )
        )

    for f in features[:2]:
        logger.info("*** Example ***")
        logger.info("feature: %s" % f)

    return features


processors = {"race": RaceProcessor, "swag": SwagProcessor, "arc": ArcProcessor}


MULTIPLE_CHOICE_TASKS_NUM_LABELS = {"race", 4, "swag", 4, "arc", 4}