train.py

#!/usr/bin/env python
"""
 * Copyright (c) 2021, salesforce.com, inc.
 * All rights reserved.
 * SPDX-License-Identifier: BSD-3-Clause
 * For full license text, see the LICENSE file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
#
# Based on https://github.com/huggingface/transformers/blob/master/examples/pytorch/summarization/run_summarization.py
#
# coding=utf-8
# Copyright 2021 The HuggingFace 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.
"""
Fine-tuning the library models for sequence to sequence.
"""
# You can also adapt this script on your own sequence to sequence task. Pointers for this are left as comments.

# Change log
# 10/29/2021 | Jesse Vig | Customized  to multiencoder. See transformers/ directory.
#

import logging
import os
import pickle
import sys
from dataclasses import dataclass, field
from statistics import mean
from typing import Optional

import datasets
import nltk  # Here to have a nice missing dependency error message early on
import numpy as np
import transformers
from datasets import load_dataset, load_metric
from filelock import FileLock
from transformers import (
    AutoConfig,
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    DataCollatorForSeq2Seq,
    HfArgumentParser,
    Seq2SeqTrainer,
    Seq2SeqTrainingArguments,
    set_seed,
)
from transformers.file_utils import is_offline_mode
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils.versions import require_version

from dataset import MultiEncoderDataset
from models import BartForMultiConditionalGeneration

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
# check_min_version("4.12.0.dev0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/summarization/requirements.txt")

logger = logging.getLogger(__name__)

try:
    nltk.data.find("tokenizers/punkt")
except (LookupError, OSError):
    if is_offline_mode():
        raise LookupError(
            "Offline mode: run this script without TRANSFORMERS_OFFLINE first to download nltk data files"
        )
    with FileLock(".lock") as lock:
        nltk.download("punkt", quiet=True)


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

    model_name_or_path: str = field(
        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 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)."
        },
    )
    resize_position_embeddings: Optional[bool] = field(
        default=None,
        metadata={
            "help": "Whether to automatically resize the position embeddings if `max_source_length` exceeds "
                    "the model's position embeddings."
        },
    )
    from_pt: bool = field(
        default=False,
        metadata={
            "help": "Whether to load the model checkpoint from .pt file"
        },
    )
    multiencoder_type: Optional[str] = field(
        default=None,
        metadata={"help": "Currently only 'bart' is supported"},
    )
    multiencoder_max_num_chunks: Optional[int] = field(
        default=None,
        metadata={
            "help": "Max passages/encoders to use in multiencoder"
        },
    )
    multiencoder_stride: bool = field(
        default=False,
        metadata={
            "help": "Whether to stride"
        },
    )


@dataclass
class DataTrainingArguments:
    """
    Arguments pertaining to what data we are going to input our model for training and eval.
    """

    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)."}
    )
    text_column: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the column in the datasets containing the full texts (for summarization)."},
    )
    summary_column: Optional[str] = field(
        default=None,
        metadata={"help": "The name of the column in the datasets containing the summaries (for summarization)."},
    )
    train_file: Optional[str] = field(
        default=None, metadata={"help": "The input training data file (a jsonlines or csv file)."}
    )
    validation_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input evaluation data file to evaluate the metrics (rouge) on "
                    "(a jsonlines or csv file)."
        },
    )
    test_file: Optional[str] = field(
        default=None,
        metadata={
            "help": "An optional input test data file to evaluate the metrics (rouge) on " "(a jsonlines or csv file)."
        },
    )
    overwrite_cache: bool = field(
        default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
    )
    preprocessing_num_workers: Optional[int] = field(
        default=None,
        metadata={"help": "The number of processes to use for the preprocessing."},
    )
    max_source_length: Optional[int] = field(
        default=1024,
        metadata={
            "help": "The maximum total input sequence length after tokenization. Sequences longer "
                    "than this will be truncated, sequences shorter will be padded."
        },
    )
    max_target_length: Optional[int] = field(
        default=128,
        metadata={
            "help": "The maximum total sequence length for target text after tokenization. Sequences longer "
                    "than this will be truncated, sequences shorter will be padded."
        },
    )
    val_max_target_length: Optional[int] = field(
        default=None,
        metadata={
            "help": "The maximum total sequence length for validation target text after tokenization. Sequences longer "
                    "than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`."
                    "This argument is also used to override the ``max_length`` param of ``model.generate``, which is used "
                    "during ``evaluate`` and ``predict``."
        },
    )
    pad_to_max_length: bool = field(
        default=False,
        metadata={
            "help": "Whether to pad all samples to model maximum sentence length. "
                    "If False, will pad the samples dynamically when batching to the maximum length in the batch. More "
                    "efficient on GPU but very bad for TPU."
        },
    )
    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."
        },
    )
    num_beams: Optional[int] = field(
        default=None,
        metadata={
            "help": "Number of beams to use for evaluation. This argument will be passed to ``model.generate``, "
                    "which is used during ``evaluate`` and ``predict``."
        },
    )
    ignore_pad_token_for_loss: bool = field(
        default=True,
        metadata={
            "help": "Whether to ignore the tokens corresponding to padded labels in the loss computation or not."
        },
    )
    source_prefix: Optional[str] = field(
        default=None, metadata={"help": "A prefix to add before every source text (useful for T5 models)."}
    )
    compute_rouge_for_train: bool = field(
        default=True, metadata={"help": "Whether to compute rouge in every eval cycle within training loop"}
    )
    prediction_path: Optional[str] = field(
        default=None, metadata={"help": "Path to output prediction file that will be generated"}
    )

    def __post_init__(self):
        if self.dataset_name is None and self.train_file is None and self.validation_file is None \
                and self.test_file is None:
            raise ValueError("Need either a dataset name or a training/validation/test file.")
        else:
            if self.train_file is not None:
                extension = self.train_file.split(".")[-1]
                assert extension in ["csv", "json", "jsonl", "pickle"],\
                    "`train_file` should be a csv, json, jsonl, or pickle file."
            if self.validation_file is not None:
                extension = self.validation_file.split(".")[-1]
                assert extension in ["csv", "json", "jsonl", "pickle"],\
                    "`validation_file` should be a csv, json, jsonl, or pickle file."
        if self.val_max_target_length is None:
            self.val_max_target_length = self.max_target_length


summarization_name_mapping = {
    "amazon_reviews_multi": ("review_body", "review_title"),
    "big_patent": ("description", "abstract"),
    "cnn_dailymail": ("article", "highlights"),
    "orange_sum": ("text", "summary"),
    "pn_summary": ("article", "summary"),
    "psc": ("extract_text", "summary_text"),
    "samsum": ("dialogue", "summary"),
    "thaisum": ("body", "summary"),
    "xglue": ("news_body", "news_title"),
    "xsum": ("document", "summary"),
    "wiki_summary": ("article", "highlights"),
}


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.
    print("got here")
    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
    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)],
    )
    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # 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}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    if data_args.source_prefix is None and model_args.model_name_or_path in [
        "t5-small",
        "t5-base",
        "t5-large",
        "t5-3b",
        "t5-11b",
    ]:
        logger.warning(
            "You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with "
            "`--source_prefix 'summarize: ' `"
        )

    # 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(training_args.output_dir)
        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."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

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

    config = AutoConfig.from_pretrained(
        model_args.config_name if model_args.config_name else model_args.model_name_or_path,
        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,
    )

    # Load pretrained model and tokenizer
    #
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.

    if model_args.multiencoder_type is not None:
        if model_args.multiencoder_type == 'bart':
            model_class = BartForMultiConditionalGeneration
        else:
            raise ValueError(f"Invalid multiencoder_type: {model_args.multiencoder_type}")
    else:
        model_class = AutoModelForSeq2SeqLM
    model = model_class.from_pretrained(
        model_args.model_name_or_path,
        from_tf=bool(".ckpt" in model_args.model_name_or_path),
        config=config,
        cache_dir=model_args.cache_dir,
        revision=model_args.model_revision,
        use_auth_token=True if model_args.use_auth_token else None
    )

    model.resize_token_embeddings(len(tokenizer))

    if model.config.decoder_start_token_id is None:
        raise ValueError("Make sure that `config.decoder_start_token_id` is correctly defined")

    if (
        hasattr(model.config, "max_position_embeddings")
        and model.config.max_position_embeddings < data_args.max_source_length
    ):
        if model_args.resize_position_embeddings is None:
            logger.warning(
                f"Increasing the model's number of position embedding vectors from {model.config.max_position_embeddings} "
                f"to {data_args.max_source_length}."
            )
            model.resize_position_embeddings(data_args.max_source_length)
        elif model_args.resize_position_embeddings:
            model.resize_position_embeddings(data_args.max_source_length)
        else:
            raise ValueError(
                f"`--max_source_length` is set to {data_args.max_source_length}, but the model only has {model.config.max_position_embeddings}"
                f" position encodings. Consider either reducing `--max_source_length` to {model.config.max_position_embeddings} or to automatically "
                "resize the model's position encodings by passing `--resize_position_embeddings`."
            )

    prefix = data_args.source_prefix if data_args.source_prefix is not None else ""

    if training_args.label_smoothing_factor > 0 and not hasattr(model, "prepare_decoder_input_ids_from_labels"):
        logger.warning(
            "label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for"
            f"`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory"
        )

    # Load dataset
    if model_args.multiencoder_type is not None:
        #Load dataset for multiencoder model
        if data_args.pad_to_max_length:
            raise NotImplementedError
        if not model_args.multiencoder_max_num_chunks:
            raise ValueError("Max num chunks required for multiencoder")

        if training_args.do_train:
            logger.info("Loading training set")
            if data_args.train_file.endswith('.pickle'):
                with open(data_args.train_file, 'rb') as f:
                    train_dataset = pickle.load(f)
            else:
                train_dataset = MultiEncoderDataset(
                    data_path=data_args.train_file,
                    tokenizer=tokenizer,
                    chunk_size=data_args.max_source_length,
                    max_num_chunks=model_args.multiencoder_max_num_chunks,
                    max_target_length=data_args.max_target_length,
                    stride=model_args.multiencoder_stride,
                    num_samples=data_args.max_train_samples,
                    ignore_pad_token_for_loss=data_args.ignore_pad_token_for_loss,
                )

        if training_args.do_eval:
            logger.info("Loading eval set")
            if data_args.validation_file.endswith('.pickle'):
                with open(data_args.validation_file, 'rb') as f:
                    eval_dataset = pickle.load(f)
            else:
                eval_dataset = MultiEncoderDataset(
                    data_path=data_args.validation_file,
                    tokenizer=tokenizer,
                    chunk_size=data_args.max_source_length,
                    max_num_chunks=model_args.multiencoder_max_num_chunks,
                    max_target_length=data_args.val_max_target_length,
                    stride=model_args.multiencoder_stride,
                    num_samples=data_args.max_eval_samples,
                    ignore_pad_token_for_loss=data_args.ignore_pad_token_for_loss
                )

        if training_args.do_predict:
            logger.info("Loading predict set")
            if data_args.test_file.endswith('.pickle'):
                with open(data_args.test_file, 'rb') as f:
                    predict_dataset = pickle.load(f)
            else:
                predict_dataset = MultiEncoderDataset(
                    data_path=data_args.test_file,
                    tokenizer=tokenizer,
                    chunk_size=data_args.max_source_length,
                    max_num_chunks=model_args.multiencoder_max_num_chunks,
                    max_target_length=data_args.val_max_target_length,
                    stride=model_args.multiencoder_stride,
                    num_samples=data_args.max_predict_samples,
                    ignore_pad_token_for_loss=data_args.ignore_pad_token_for_loss
                )

        data_collator = None
    else:
        # Load datasets for standard models
        #
        # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
        # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
        # (the dataset will be downloaded automatically from the datasets Hub).
        #
        # For CSV/JSON files this script will use the first column for the full texts and the second column for the
        # summaries (unless you specify column names for this with the `text_column` and `summary_column` arguments).
        #
        # In distributed training, the load_dataset function guarantee that only one local process can concurrently
        # download the dataset.
        if data_args.dataset_name is not None:
            # Downloading and loading a dataset from the hub.
            raw_datasets = load_dataset(
                data_args.dataset_name, data_args.dataset_config_name, cache_dir=model_args.cache_dir
            )
        else:
            data_files = {}
            if data_args.train_file is not None:
                data_files["train"] = data_args.train_file
                extension = data_args.train_file.split(".")[-1]
            if data_args.validation_file is not None:
                data_files["validation"] = data_args.validation_file
                extension = data_args.validation_file.split(".")[-1]
            if data_args.test_file is not None:
                data_files["test"] = data_args.test_file
                extension = data_args.test_file.split(".")[-1]
            raw_datasets = load_dataset(extension, data_files=data_files, cache_dir=model_args.cache_dir)
        # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
        # https://huggingface.co/docs/datasets/loading_datasets.html.

        # Preprocessing the datasets.
        # We need to tokenize inputs and targets.
        if training_args.do_train:
            column_names = raw_datasets["train"].column_names
        elif training_args.do_eval:
            column_names = raw_datasets["validation"].column_names
        elif training_args.do_predict:
            column_names = raw_datasets["test"].column_names
        else:
            logger.info("There is nothing to do. Please pass `do_train`, `do_eval` and/or `do_predict`.")
            return

        # Get the column names for input/target.
        dataset_columns = summarization_name_mapping.get(data_args.dataset_name, None)
        if data_args.text_column is None:
            text_column = dataset_columns[0] if dataset_columns is not None else column_names[0]
        else:
            text_column = data_args.text_column
            if text_column not in column_names:
                raise ValueError(
                    f"--text_column' value '{data_args.text_column}' needs to be one of: {', '.join(column_names)}"
                )
        if data_args.summary_column is None:
            summary_column = dataset_columns[1] if dataset_columns is not None else column_names[1]
        else:
            summary_column = data_args.summary_column
            if summary_column not in column_names:
                raise ValueError(
                    f"--summary_column' value '{data_args.summary_column}' needs to be one of: {', '.join(column_names)}"
                )

        # Temporarily set max_target_length for training.

        max_target_length = data_args.max_target_length
        padding = "max_length" if data_args.pad_to_max_length else False

        def preprocess_function(examples):
            inputs = examples[text_column]
            targets = examples[summary_column]
            inputs = [prefix + inp for inp in inputs]
            model_inputs = tokenizer(inputs, max_length=data_args.max_source_length, padding=padding, truncation=True)

            # Setup the tokenizer for targets
            with tokenizer.as_target_tokenizer():
                labels = tokenizer(targets, max_length=max_target_length, padding=padding, truncation=True)

            # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
            # padding in the loss.
            if padding == "max_length" and data_args.ignore_pad_token_for_loss:
                labels["input_ids"] = [
                    [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels["input_ids"]
                ]

            model_inputs["labels"] = labels["input_ids"]
            return model_inputs

        if training_args.do_train:
            if "train" not in raw_datasets:
                raise ValueError("--do_train requires a train dataset")
            train_dataset = raw_datasets["train"]
            if data_args.max_train_samples is not None:
                train_dataset = train_dataset.select(range(data_args.max_train_samples))
            with training_args.main_process_first(desc="train dataset map pre-processing"):
                train_dataset = train_dataset.map(
                    preprocess_function,
                    batched=True,
                    num_proc=data_args.preprocessing_num_workers,
                    remove_columns=column_names,
                    load_from_cache_file=not data_args.overwrite_cache,
                    desc="Running tokenizer on train dataset",
                )

        if training_args.do_eval:
            max_target_length = data_args.val_max_target_length
            if "validation" not in raw_datasets:
                raise ValueError("--do_eval requires a validation dataset")
            eval_dataset = raw_datasets["validation"]
            if data_args.max_eval_samples is not None:
                eval_dataset = eval_dataset.select(range(data_args.max_eval_samples))
            with training_args.main_process_first(desc="validation dataset map pre-processing"):
                eval_dataset = eval_dataset.map(
                    preprocess_function,
                    batched=True,
                    num_proc=data_args.preprocessing_num_workers,
                    remove_columns=column_names,
                    load_from_cache_file=not data_args.overwrite_cache,
                    desc="Running tokenizer on validation dataset",
                )
                print('size of labels tensor', max(len(item['labels']) for item in eval_dataset))

        if training_args.do_predict:
            max_target_length = data_args.val_max_target_length
            if "test" not in raw_datasets:
                raise ValueError("--do_predict requires a test dataset")
            predict_dataset = raw_datasets["test"]
            if data_args.max_predict_samples is not None:
                predict_dataset = predict_dataset.select(range(data_args.max_predict_samples))
            with training_args.main_process_first(desc="prediction dataset map pre-processing"):
                predict_dataset = predict_dataset.map(
                    preprocess_function,
                    batched=True,
                    num_proc=data_args.preprocessing_num_workers,
                    remove_columns=column_names,
                    load_from_cache_file=not data_args.overwrite_cache,
                    desc="Running tokenizer on prediction dataset",
                )

        # Data collator
        label_pad_token_id = -100 if data_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
        data_collator = DataCollatorForSeq2Seq(
            tokenizer,
            model=model,
            label_pad_token_id=label_pad_token_id,
            pad_to_multiple_of=8 if training_args.fp16 else None,
        )

    # Metric
    metric = load_metric("rouge")

    def postprocess_text(preds, labels):
        preds = [pred.strip() for pred in preds]
        labels = [label.strip() for label in labels]

        # rougeLSum expects newline after each sentence
        preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
        labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]

        return preds, labels

    def compute_metrics(eval_preds):
        preds, labels = eval_preds
        if isinstance(preds, tuple):
            preds = preds[0]
        decoded_preds = tokenizer.batch_decode(preds, skip_special_tokens=True)
        if data_args.ignore_pad_token_for_loss:
            # Replace -100 in the labels as we can't decode them.
            labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
        decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)

        # Some simple post-processing
        decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)

        result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
        result = {key: value.mid.fmeasure * 100 for key, value in result.items()}
        result['eval_mean_rouge'] = mean([result[k] for k in ['rouge1', 'rouge2', 'rougeLsum']])

        prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in preds]
        result["gen_len"] = np.mean(prediction_lens)
        result = {k: round(v, 4) for k, v in result.items()}
        logger.info("computing metrics")
        return result

    # Initialize our Trainer
    trainer = Seq2SeqTrainer(
        model=model,
        args=training_args,
        train_dataset=train_dataset if training_args.do_train else None,
        eval_dataset=eval_dataset if training_args.do_eval else None,
        tokenizer=tokenizer,
        data_collator=data_collator,
        compute_metrics=(
            compute_metrics if training_args.predict_with_generate and data_args.compute_rouge_for_train else None
        )
    )

    # Training
    if training_args.do_train:
        logger.info("*** Train ***")
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()  # Saves the tokenizer too for easy upload

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

    # Evaluation
    results = {}
    max_length = (
        training_args.generation_max_length
        if training_args.generation_max_length is not None
        else data_args.val_max_target_length
    )
    num_beams = data_args.num_beams if data_args.num_beams is not None else training_args.generation_num_beams
    if training_args.do_eval:
        logger.info("*** Evaluate ***")

        # Set compute_metrics here in case it wasn't set above
        trainer.compute_metrics = compute_metrics if training_args.predict_with_generate else None
        metrics = trainer.evaluate(max_length=max_length, num_beams=num_beams, metric_key_prefix="eval")
        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 ***")

        predict_results = trainer.predict(
            predict_dataset, metric_key_prefix="predict", max_length=max_length, num_beams=num_beams
        )
        metrics = predict_results.metrics
        max_predict_samples = (
            data_args.max_predict_samples if data_args.max_predict_samples is not None else len(predict_dataset)
        )
        metrics["predict_samples"] = min(max_predict_samples, len(predict_dataset))

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

        if trainer.is_world_process_zero():
            if training_args.predict_with_generate:
                predictions = tokenizer.batch_decode(
                    predict_results.predictions, skip_special_tokens=True, clean_up_tokenization_spaces=True
                )
                predictions = [pred.strip() for pred in predictions]
                if data_args.prediction_path:
                    prediction_path = data_args.prediction_path
                else:
                    prediction_path = os.path.join(training_args.output_dir, "test.predictions")
                # print('Writing prediction file to', output_prediction_file)
                with open(prediction_path, "w") as writer:
                    writer.write("\n".join(predictions))

    kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "summarization"}
    if data_args.dataset_name is not None:
        kwargs["dataset_tags"] = data_args.dataset_name
        if data_args.dataset_config_name is not None:
            kwargs["dataset_args"] = data_args.dataset_config_name
            kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
        else:
            kwargs["dataset"] = data_args.dataset_name

    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)

    return results


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


if __name__ == "__main__":
    main()