run_glue.py

#!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. 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.
""" Finetuning the library models for sequence classification on GLUE."""
# You can also adapt this script on your own text classification task. Pointers for this are left as comments.

import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import numpy as np
from datasets import load_dataset, load_metric
import torch
import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    PretrainedConfig,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from models.multiplexing import RobertaSequenceClassificationMuxed
from models.utils import get_last_checkpoint_trainerstate_robust
from models.trainer import MuxTrainer
import re

task_to_keys = {
    "cola": ("sentence", None),
    "mnli": ("premise", "hypothesis"),
    "mrpc": ("sentence1", "sentence2"),
    "qnli": ("question", "sentence"),
    "qqp": ("question1", "question2"),
    "rte": ("sentence1", "sentence2"),
    "sst2": ("sentence", None),
    "stsb": ("sentence1", "sentence2"),
    "wnli": ("sentence1", "sentence2"),
}

logger = logging.getLogger(__name__)


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    Using `HfArgumentParser` we can turn this class
    into argparse arguments to be able to specify them on
    the command line.
    """

    task_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "The name of the task to train on: "
            + ", ".join(task_to_keys.keys())
        },
    )
    dataset_name: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the dataset to use (via the datasets library)."},
    )
    dataset_config_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "The configuration name of the dataset to use (via the datasets library)."
        },
    )
    max_seq_length: int = field(
        default=128,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded."
        },
    )
    overwrite_cache: bool = field(
        default=False,
        metadata={"help": "Overwrite the cached preprocessed datasets or not."},
    )
    pad_to_max_length: bool = field(
        default=True,
        metadata={
            "help": "Whether to pad all samples to `max_seq_length`. "
            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
        },
    )
    max_train_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of training examples to this "
            "value if set."
        },
    )
    max_eval_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
            "value if set."
        },
    )
    max_predict_samples: Optional[int] = field(
        default=None,
        metadata={
            "help": "For debugging purposes or quicker training, truncate the number of prediction examples to this "
            "value if set."
        },
    )
    train_file: Optional[str] = field(
        default=None,
        metadata={"help": "A csv or a json file containing the training data."},
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={"help": "A csv or a json file containing the validation data."},
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={"help": "A csv or a json file containing the test data."},
    )

    def __post_init__(self):
        if self.task_name is not None:
            self.task_name = self.task_name.lower()
            if self.task_name not in task_to_keys.keys():
                raise ValueError(
                    "Unknown task, you should pick one in "
                    + ",".join(task_to_keys.keys())
                )
        elif self.dataset_name is not None:
            pass
        elif self.train_file is None or self.validation_file is None:
            raise ValueError(
                "Need either a GLUE task, a training/validation file or a dataset name."
            )
        else:
            train_extension = self.train_file.split(".")[-1]
            assert train_extension in [
                "csv",
                "json",
            ], "`train_file` should be a csv or a json file."
            validation_extension = self.validation_file.split(".")[-1]
            assert (
                validation_extension == train_extension
            ), "`validation_file` should have the same extension (csv or json) as `train_file`."


@dataclass
class ModelArguments:
    """
    Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
    """

    model_name_or_path: str = field(
        default=None,
        metadata={
            "help": "Path to pretrained model or model identifier from huggingface.co/models"
        }
    )
    config_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "Pretrained config name or path if not the same as model_name"
        },
    )
    tokenizer_name: Optional[str] = field(
        default=None,
        metadata={
            "help": "Pretrained tokenizer name or path if not the same as model_name"
        },
    )
    cache_dir: Optional[str] = field(
        default=None,
        metadata={
            "help": "Where do you want to store the pretrained models downloaded from huggingface.co"
        },
    )
    use_fast_tokenizer: bool = field(
        default=True,
        metadata={
            "help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."
        },
    )
    model_revision: str = field(
        default="main",
        metadata={
            "help": "The specific model version to use (can be a branch name, tag name or commit id)."
        },
    )
    use_auth_token: bool = field(
        default=False,
        metadata={
            "help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
            "with private models)."
        },
    )
    # multi instance arguments
    num_instances: Optional[int] = field(
        default=5,
        metadata={"help": "Number of instances i.e. N"},
    )
    muxing_variant: Optional[str] = field(
        default="gaussian_hadamard",
        metadata={"help": "muxing variant; choose from gaussian_hadamard or random_ortho or binary_hadamard"},
    )
    demuxing_variant: Optional[str] = field(
        default="index",
        metadata={"help": "demuxing variant, choose from  'index' or 'mlp'"},
    )
    should_mux: Optional[int] = field(
        default=1,
        metadata={"help": "whether to mux, turn off for non-multiplexed baselines"},
    )
    retrieval_percentage: Optional[float] = field(
        default=1.0,
        metadata={"help": "percentage of tokens to retrieve during inference"},
    )
    retrieval_pretraining: Optional[int] = field(
        default=0,
        metadata={"help": "Retrieval Pretraining"},
    )
    gaussian_hadamard_norm: Optional[float] = field(
        default=1,
        metadata={"help": "Norm of sentence embeddings if we use random projections"},
    )
    binary_hadamard_epsilon: Optional[float] = field(
        default=0,
        metadata={"help": "Percentage intersection among binary vectors, default is no intersection"},
    )
    retrieval_loss_coeff: Optional[float] = field(
        default=0.1,
        metadata={"help": "Coefficient for retrieval loss"},
    )    
    task_loss_coeff: Optional[float] = field(
        default=0.9,
        metadata={"help": "Coefficient for task loss"},
    )
    learn_muxing: Optional[int] = field(
        default=0,
        metadata={"help": "whether instance embeddings are learnt or not"},
    )


def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.
    parser = HfArgumentParser(
        (ModelArguments, DataTrainingArguments, TrainingArguments)
    )
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(
            json_file=os.path.abspath(sys.argv[1])
        )
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )
    logger.setLevel(logging.INFO)

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    # Set the verbosity to info of the Transformers logger (on main process only):
    transformers.utils.logging.set_verbosity_info()
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if (
        os.path.isdir(training_args.output_dir)
        and training_args.do_train
        and not training_args.overwrite_output_dir
    ):
        last_checkpoint = get_last_checkpoint_trainerstate_robust(training_args.output_dir)
        print("last checkpoint", last_checkpoint)

        # if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
        #     raise ValueError(
        #         f"Output directory ({training_args.output_dir}) already exists and is not empty. "
        #         "Use --overwrite_output_dir to overcome."
        #     )


    # Set seed before initializing model.
    set_seed(training_args.seed)

    # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
    # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
    #
    # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
    # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
    # label if at least two columns are provided.
    #
    # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
    # single column. You can easily tweak this behavior (see below)
    #
    # In distributed training, the load_dataset function guarantee that only one local process can concurrently
    # download the dataset.
    if data_args.task_name is not None:
        # Downloading and loading a dataset from the hub.
        datasets = load_dataset(
            "glue", data_args.task_name, cache_dir=model_args.cache_dir
        )
    elif data_args.dataset_name is not None:
        # Downloading and loading a dataset from the hub.
        datasets = load_dataset(
            data_args.dataset_name,
            data_args.dataset_config_name,
            cache_dir=model_args.cache_dir,
        )
    else:
        # Loading a dataset from your local files.
        # CSV/JSON training and evaluation files are needed.
        data_files = {
            "train": data_args.train_file,
            "validation": data_args.validation_file,
        }

        # Get the test dataset: you can provide your own CSV/JSON test file (see below)
        # when you use `do_predict` without specifying a GLUE benchmark task.
        if training_args.do_predict:
            if data_args.test_file is not None:
                train_extension = data_args.train_file.split(".")[-1]
                test_extension = data_args.test_file.split(".")[-1]
                assert (
                    test_extension == train_extension
                ), "`test_file` should have the same extension (csv or json) as `train_file`."
                data_files["test"] = data_args.test_file
            else:
                raise ValueError(
                    "Need either a GLUE task or a test file for `do_predict`."
                )

        for key in data_files.keys():
            logger.info(f"load a local file for {key}: {data_files[key]}")

        if data_args.train_file.endswith(".csv"):
            # Loading a dataset from local csv files
            datasets = load_dataset(
                "csv", data_files=data_files, cache_dir=model_args.cache_dir
            )
        else:
            # Loading a dataset from local json files
            datasets = load_dataset(
                "json", data_files=data_files, cache_dir=model_args.cache_dir
            )
    # See more about loading any type of standard or custom dataset at
    # https://huggingface.co/docs/datasets/loading_datasets.html.

    # Labels
    if data_args.task_name is not None:
        is_regression = data_args.task_name == "stsb"
        if not is_regression:
            label_list = datasets["train"].features["label"].names
            num_labels = len(label_list)
        else:
            num_labels = 1
    else:
        if model_args.retrieval_pretraining:
            # add dummy value here
            num_labels = 2
            is_regression = False
            label_list = ["dummy1", "dummy2"]
        else:
            # Trying to have good defaults here, don't hesitate to tweak to your needs.
            is_regression = datasets["train"].features["label"].dtype in [
                "float32",
                "float64",
            ]
            if is_regression:
                num_labels = 1
            else:
                # A useful fast method:
                # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
                label_list = datasets["train"].unique("label")
                label_list.sort()  # Let's sort it for determinism
                num_labels = len(label_list)
    #
    # Load pretrained model and tokenizer
    #
    # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config = AutoConfig.from_pretrained(
        model_args.config_name
        if model_args.config_name
        else model_args.model_name_or_path,
        num_labels=num_labels,
        finetuning_task=data_args.task_name,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )
    tokenizer = AutoTokenizer.from_pretrained(
        model_args.tokenizer_name
        if model_args.tokenizer_name
        else model_args.model_name_or_path,
        cache_dir=model_args.cache_dir,
        use_fast=model_args.use_fast_tokenizer,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    config.num_instances = model_args.num_instances
    config.muxing_variant = model_args.muxing_variant
    config.demuxing_variant = model_args.demuxing_variant
    config.retrieval_percentage = model_args.retrieval_percentage
    config.gaussian_hadamard_norm = model_args.gaussian_hadamard_norm
    config.binary_hadamard_epsilon = model_args.binary_hadamard_epsilon
    config.retrieval_loss_coeff = model_args.retrieval_loss_coeff
    config.task_loss_coeff = model_args.task_loss_coeff
    config.learn_muxing = model_args.learn_muxing

    model_path_supplied = model_args.model_name_or_path is not None
    if model_args.should_mux:
        
        if model_path_supplied:
            model = RobertaSequenceClassificationMuxed.from_pretrained(model_args.model_name_or_path, config=config)
        else:
            model = RobertaSequenceClassificationMuxed(config=config)
    else:
        # non-multiplexed baseline
        if model_path_supplied:
            model = AutoModelForSequenceClassification.from_pretrained(model_args.model_name_or_path, config=config)
        else:
            model = AutoModelForSequenceClassification.from_config(config=config)

    if data_args.task_name is not None:
        sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
    else:
        # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
        non_label_column_names = [
            name for name in datasets["train"].column_names if name != "label"
        ]
        if (
            "sentence1" in non_label_column_names
            and "sentence2" in non_label_column_names
        ):
            sentence1_key, sentence2_key = "sentence1", "sentence2"
        else:
            if len(non_label_column_names) >= 2:
                sentence1_key, sentence2_key = non_label_column_names[:2]
            else:
                sentence1_key, sentence2_key = non_label_column_names[0], None

    # Padding strategy
    if data_args.pad_to_max_length:
        padding = "max_length"
    else:
        # We will pad later, dynamically at batch creation, to the max sequence length in each batch
        padding = False

    # Some models have set the order of the labels to use, so let's make sure we do use it.
    label_to_id = None
    if (
        model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
        and data_args.task_name is not None
        and not is_regression
    ):
        # Some have all caps in their config, some don't.
        label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
        if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
            label_to_id = {
                i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)
            }
        else:
            logger.warning(
                "Your model seems to have been trained with labels, but they don't match the dataset: ",
                f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
                "\nIgnoring the model labels as a result.",
            )
    elif data_args.task_name is None and not is_regression:
        label_to_id = {v: i for i, v in enumerate(label_list)}

    if label_to_id is not None:
        model.config.label2id = label_to_id
        model.config.id2label = {id: label for label, id in config.label2id.items()}

    if data_args.max_seq_length > tokenizer.model_max_length:
        logger.warning(
            f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
            f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
        )
    max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)

    def preprocess_function(examples):
        # Tokenize the texts
        args = (
            (examples[sentence1_key],)
            if sentence2_key is None
            else (examples[sentence1_key], examples[sentence2_key])
        )
        result = tokenizer(
            *args, padding=padding, max_length=max_seq_length, truncation=True
        )

        # Map labels to IDs (not necessary for GLUE tasks)
        if label_to_id is not None and "label" in examples:
            result["label"] = [
                (label_to_id[l] if l != -1 else -1) for l in examples["label"]
            ]
        return result

    if model_args.retrieval_pretraining:
        # process wikitext dataset
        column_names = datasets["train"].column_names

        text_column_name = "text"
        def tokenize_function(examples):
            examples["text"] = [line for line in examples["text"] if line is not None and len(line) > 0 and not line.isspace()]
            return tokenizer(examples[text_column_name], return_special_tokens_mask=True)

        tokenized_datasets = datasets.map(
            tokenize_function,
            batched=True,
            # num_proc=data_args.preprocessing_num_workers,
            remove_columns=column_names,
            load_from_cache_file=True,
        )

        # Main data processing function that will concatenate all texts from our dataset and generate chunks of
        # max_seq_length.
        def group_texts(examples):
            # Concatenate all texts.
            concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
            total_length = len(concatenated_examples[list(examples.keys())[0]])
            # We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
            # customize this part to your needs.
            total_length = (total_length // max_seq_length) * max_seq_length
            # Split by chunks of max_len.
            result = {
                k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)]
                for k, t in concatenated_examples.items()
            }
            random_key = list(result.keys())[0]
            len_result = len(result[random_key])
            result["label"] = [random.randint(0, num_labels-1) for _ in range(len_result)]
            return result

        # Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a
        # remainder for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value
        # might be slower to preprocess.
        #
        # To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
        # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.map

        tokenized_datasets = tokenized_datasets.map(
            group_texts,
            batched=True,
            # num_proc=data_args.preprocessing_num_workers,
            load_from_cache_file=True,
        )
        datasets = tokenized_datasets

    else:
        datasets = datasets.map(
            preprocess_function,
            batched=True,
            load_from_cache_file=not data_args.overwrite_cache,
            # desc="Running tokenizer on dataset",
        )
    # if training_args.do_train:
    if "train" not in datasets:
        raise ValueError("--do_train requires a train dataset")
    train_dataset = datasets["train"]
    if data_args.max_train_samples is not None:
        train_dataset = train_dataset.select(range(data_args.max_train_samples))

    # if training_args.do_eval:
    if "validation" not in datasets and "validation_matched" not in datasets:
        raise ValueError("--do_eval requires a validation dataset")
    eval_dataset = datasets[
        "validation_matched" if data_args.task_name == "mnli" else "validation"
    ]
    if data_args.max_eval_samples is not None:
        eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))

    if (
        training_args.do_predict
        or data_args.task_name is not None
        or data_args.test_file is not None
    ):
        if "test" not in datasets and "test_matched" not in datasets:
            raise ValueError("--do_predict requires a test dataset")
        predict_dataset = datasets[
            "test_matched" if data_args.task_name == "mnli" else "test"
        ]
        if data_args.max_predict_samples is not None:
            predict_dataset = predict_dataset.select(
                range(data_args.max_predict_samples)
            )

    # Log a few random samples from the training set:
    if training_args.do_train:
        for index in random.sample(range(len(train_dataset)), 3):
            logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")

    # Get the metric function
    if data_args.task_name is not None:
        metric = load_metric("glue", data_args.task_name)
    else:
        metric = load_metric("accuracy")

    # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
    # predictions and label_ids field) and has to return a dictionary string to float.
    def compute_metrics(p: EvalPrediction):
        preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
        preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
        if data_args.task_name is not None:
            result = metric.compute(predictions=preds, references=p.label_ids)
            if len(result) > 1:
                result["combined_score"] = np.mean(list(result.values())).item()
            return result
        elif is_regression:
            return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
        else:
            return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}

    # Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
    if data_args.pad_to_max_length:
        data_collator = default_data_collator
    elif training_args.fp16:
        data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
    else:
        data_collator = None

    trainer = MuxTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset,
        eval_dataset=eval_dataset,
        compute_metrics=compute_metrics,
        tokenizer=tokenizer,
        data_collator=data_collator,
    )
    # Training
    if training_args.do_train:
        logger.info("*** Train ***")

        checkpoint = None
        if last_checkpoint is not None:
            checkpoint = last_checkpoint
        print("checkpoint", checkpoint)
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        metrics = train_result.metrics
        max_train_samples = (
            data_args.max_train_samples
            if data_args.max_train_samples is not None
            else len(train_dataset)
        )
        metrics["train_samples"] = min(max_train_samples, len(train_dataset))

        trainer.save_model()  # Saves the tokenizer too for easy upload

        trainer.log_metrics("train", metrics)
        trainer.save_metrics("train", metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        logger.info("*** Evaluate ***")
        # Loop to handle MNLI double evaluation (matched, mis-matched)
        tasks = [data_args.task_name]
        eval_datasets = [eval_dataset]
        # if data_args.task_name == "mnli":
        #     tasks.append("mnli-mm")
        #     eval_datasets.append(datasets["validation_mismatched"])

        for eval_dataset, task in zip(eval_datasets, tasks):
            metrics = trainer.evaluate(eval_dataset=eval_dataset, speed_metrics=True)

            max_eval_samples = (
                data_args.max_eval_samples
                if data_args.max_eval_samples is not None
                else len(eval_dataset)
            )
            metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))

            trainer.log_metrics("eval", metrics)
            trainer.save_metrics("eval", metrics)

    if training_args.do_predict:
        logger.info("*** Predict ***")

        # Loop to handle MNLI double evaluation (matched, mis-matched)
        tasks = [data_args.task_name]
        predict_datasets = [predict_dataset]
        if data_args.task_name == "mnli":
            tasks.append("mnli-mm")
            predict_datasets.append(datasets["test_mismatched"])

        for predict_dataset, task in zip(predict_datasets, tasks):
            # Removing the `label` columns because it contains -1 and Trainer won't like that.
            predict_dataset.remove_columns_("label")
            predictions = trainer.predict(
                predict_dataset, metric_key_prefix="predict"
            ).predictions
            predictions = (
                np.squeeze(predictions)
                if is_regression
                else np.argmax(predictions, axis=1)
            )

            output_predict_file = os.path.join(
                training_args.output_dir, f"predict_results_{task}.txt"
            )
            if trainer.is_world_process_zero():
                with open(output_predict_file, "w") as writer:
                    logger.info(f"***** Predict results {task} *****")
                    writer.write("index\tprediction\n")
                    for index, item in enumerate(predictions):
                        if is_regression:
                            writer.write(f"{index}\t{item:3.3f}\n")
                        else:
                            item = label_list[item]
                            writer.write(f"{index}\t{item}\n")
            

def _mp_fn(index):
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
    main()