finetune_bart_pl.py

import argparse
import glob
import logging
import os
import time
import warnings
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple

import numpy as np
import torch
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from lightning_base import BaseTransformer, add_generic_args, generic_train
from transformers import MarianTokenizer, MBartTokenizer, T5ForConditionalGeneration, get_linear_schedule_with_warmup


try:
    from .util_summarization import (
        assert_all_frozen,
        use_task_specific_params,
        lmap,
        flatten_list,
        pickle_save,
        save_git_info,
        save_json,
        freeze_params,
        calculate_rouge,
        get_git_info,
        ROUGE_KEYS,
        calculate_bleu_score,
        Seq2SeqDataset,
        TranslationDataset,
        label_smoothed_nll_loss,
    )
    from .callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
except ImportError:
    from util_summarization import (
        Seq2SeqDataset,
        TranslationDataset,
        assert_all_frozen,
        use_task_specific_params,
        lmap,
        flatten_list,
        pickle_save,
        save_git_info,
        save_json,
        freeze_params,
        calculate_rouge,
        get_git_info,
        ROUGE_KEYS,
        calculate_bleu_score,
        label_smoothed_nll_loss,
    )
    from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback

logger = logging.getLogger(__name__)


class SummarizationModule(BaseTransformer):
    mode = "summarization"
    loss_names = ["loss"]
    metric_names = ROUGE_KEYS
    val_metric = "rouge2"

    def __init__(self, hparams, **kwargs):
        super().__init__(hparams, num_labels=None, mode=self.mode, **kwargs)
        use_task_specific_params(self.model, "summarization")
        save_git_info(self.hparams.output_dir)
        self.metrics_save_path = Path(self.output_dir) / "metrics.json"
        self.hparams_save_path = Path(self.output_dir) / "hparams.pkl"
        pickle_save(self.hparams, self.hparams_save_path)
        self.step_count = 0
        self.metrics = defaultdict(list)

        self.dataset_kwargs: dict = dict(
            data_dir=self.hparams.data_dir,
            max_source_length=self.hparams.max_source_length,
            prefix=self.model.config.prefix or "",
        )
        n_observations_per_split = {
            "train": self.hparams.n_train,
            "val": self.hparams.n_val,
            "test": self.hparams.n_test,
        }
        self.n_obs = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}

        self.target_lens = {
            "train": self.hparams.max_target_length,
            "val": self.hparams.val_max_target_length,
            "test": self.hparams.test_max_target_length,
        }
        assert self.target_lens["train"] <= self.target_lens["val"], f"target_lens: {self.target_lens}"
        assert self.target_lens["train"] <= self.target_lens["test"], f"target_lens: {self.target_lens}"

        if self.hparams.freeze_embeds:
            self.freeze_embeds()
        if self.hparams.freeze_encoder:
            freeze_params(self.model.get_encoder())
            #assert_all_frozen(self.model.get_encoder())

        self.hparams.git_sha = get_git_info()["repo_sha"]
        self.num_workers = hparams.num_workers
        self.decoder_start_token_id = None
        if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer, MBartTokenizer):
            self.decoder_start_token_id = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
            self.model.config.decoder_start_token_id = self.decoder_start_token_id
        if isinstance(self.tokenizer, MBartTokenizer) or isinstance(self.tokenizer, MarianTokenizer):
            self.dataset_class = TranslationDataset
        else:
            self.dataset_class = Seq2SeqDataset

    def freeze_embeds(self):
        """Freeze token embeddings and positional embeddings for bart, just token embeddings for t5."""
        try:
            freeze_params(self.model.model.shared)
            for d in [self.model.model.encoder, self.model.model.decoder]:
                freeze_params(d.embed_positions)
                freeze_params(d.embed_tokens)
        except AttributeError:
            freeze_params(self.model.shared)
            for d in [self.model.encoder, self.model.decoder]:
                freeze_params(d.embed_tokens)

    def forward(self, input_ids, **kwargs):
        return self.model(input_ids, **kwargs)

    def ids_to_clean_text(self, generated_ids: List[int]):
        gen_text = self.tokenizer.batch_decode(
            generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True
        )
        return lmap(str.strip, gen_text)

    def _step(self, batch: dict) -> Tuple:
        pad_token_id = self.tokenizer.pad_token_id
        source_ids, source_mask, target_ids = batch["input_ids"], batch["attention_mask"], batch["decoder_input_ids"]

        if isinstance(self.model, T5ForConditionalGeneration):
            decoder_input_ids = self.model._shift_right(target_ids)
            lm_labels = target_ids
        else:
            decoder_input_ids = target_ids[:, :-1].contiguous()  # Why this line?
            lm_labels = target_ids[:, 1:].clone()  # why clone?

        outputs = self(source_ids, attention_mask=source_mask, decoder_input_ids=decoder_input_ids, use_cache=False)

        if self.hparams.label_smoothing == 0:
            # Same behavior as modeling_bart.py
            loss_fct = torch.nn.CrossEntropyLoss(ignore_index=pad_token_id)
            lm_logits = outputs[0]
            assert lm_logits.shape[-1] == self.model.config.vocab_size
            loss = loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), lm_labels.view(-1))
        else:
            lprobs = torch.nn.functional.log_softmax(outputs[0], dim=-1)
            loss, nll_loss = label_smoothed_nll_loss(
                lprobs, lm_labels, self.hparams.label_smoothing, ignore_index=pad_token_id
            )
        return (loss,)

    @property
    def pad(self) -> int:
        return self.tokenizer.pad_token_id

    def training_step(self, batch, batch_idx) -> Dict:
        loss_tensors = self._step(batch)

        logs = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
        # tokens per batch
        logs["tpb"] = batch["input_ids"].ne(self.pad).sum() + batch["decoder_input_ids"].ne(self.pad).sum()
        return {"loss": loss_tensors[0], "log": logs}

    def validation_step(self, batch, batch_idx) -> Dict:
        return self._generative_step(batch)

    def validation_epoch_end(self, outputs, prefix="val") -> Dict:
        self.step_count += 1
        losses = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
        loss = losses["loss"]
        rouges = {k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]}
        rouge_tensor: torch.FloatTensor = torch.tensor(rouges[self.val_metric]).type_as(loss)
        rouges.update({k: v.item() for k, v in losses.items()})
        losses.update(rouges)
        metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
        metrics["step_count"] = self.step_count
        self.save_metrics(metrics, prefix)  # writes to self.metrics_save_path
        preds = flatten_list([x["preds"] for x in outputs])
        return {"log": metrics, "preds": preds, f"{prefix}_loss": loss, f"{prefix}_{self.val_metric}": rouge_tensor}

    def save_metrics(self, latest_metrics, type_path) -> None:
        self.metrics[type_path].append(latest_metrics)
        save_json(self.metrics, self.metrics_save_path)

    def calc_generative_metrics(self, preds, target) -> Dict:
        return calculate_rouge(preds, target)

    def _generative_step(self, batch: dict) -> dict:
        t0 = time.time()
        generated_ids = self.model.generate(
            batch["input_ids"],
            attention_mask=batch["attention_mask"],
            use_cache=True,
            decoder_start_token_id=self.decoder_start_token_id,
        )
        gen_time = (time.time() - t0) / batch["input_ids"].shape[0]
        preds: List[str] = self.ids_to_clean_text(generated_ids)
        target: List[str] = self.ids_to_clean_text(batch["decoder_input_ids"])
        loss_tensors = self._step(batch)
        base_metrics = {name: loss for name, loss in zip(self.loss_names, loss_tensors)}
        rouge: Dict = self.calc_generative_metrics(preds, target)
        summ_len = np.mean(lmap(len, generated_ids))
        base_metrics.update(gen_time=gen_time, gen_len=summ_len, preds=preds, target=target, **rouge)
        return base_metrics

    def test_step(self, batch, batch_idx):
        return self._generative_step(batch)

    def test_epoch_end(self, outputs):
        return self.validation_epoch_end(outputs, prefix="test")

    def get_dataset(self, type_path) -> Seq2SeqDataset:
        n_obs = self.n_obs[type_path]
        max_target_length = self.target_lens[type_path]
        dataset = self.dataset_class(
            self.tokenizer,
            type_path=type_path,
            n_obs=n_obs,
            max_target_length=max_target_length,
            **self.dataset_kwargs,
        )
        return dataset

    def get_dataloader(self, type_path: str, batch_size: int, shuffle: bool = False) -> DataLoader:
        dataset = self.get_dataset(type_path)
        sampler = None
        if self.hparams.sortish_sampler and type_path == "train":
            assert self.hparams.gpus <= 1  # TODO: assert earlier
            sampler = dataset.make_sortish_sampler(batch_size)
            shuffle = False

        dataloader = DataLoader(
            dataset,
            batch_size=batch_size,
            collate_fn=dataset.collate_fn,
            shuffle=shuffle,
            num_workers=self.num_workers,
            sampler=sampler,
        )
        return dataloader

    def train_dataloader(self) -> DataLoader:
        dataloader = self.get_dataloader("train", batch_size=self.hparams.train_batch_size, shuffle=True)
        t_total = (
            (len(dataloader.dataset) // (self.hparams.train_batch_size * max(1, self.hparams.gpus)))
            // self.hparams.accumulate_grad_batches
            * float(self.hparams.max_epochs)
        )
        scheduler = get_linear_schedule_with_warmup(
            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=t_total
        )
        if max(scheduler.get_last_lr()) > 0:
            warnings.warn("All learning rates are 0")
        self.lr_scheduler = scheduler
        return dataloader

    def val_dataloader(self) -> DataLoader:
        return self.get_dataloader("val", batch_size=self.hparams.eval_batch_size)

    def test_dataloader(self) -> DataLoader:
        return self.get_dataloader("test", batch_size=self.hparams.eval_batch_size)

    @staticmethod
    def add_model_specific_args(parser, root_dir):
        BaseTransformer.add_model_specific_args(parser, root_dir)
        add_generic_args(parser, root_dir)
        parser.add_argument(
            "--max_source_length",
            default=156,
            type=int,
            help="The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded.",
        )
        parser.add_argument(
            "--max_target_length",
            default=20,
            type=int,
            help="The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded.",
        )
        parser.add_argument(
            "--val_max_target_length",
            default=20,  # these defaults are optimized for CNNDM. For xsum, see README.md.
            type=int,
            help="The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded.",
        )
        parser.add_argument(
            "--test_max_target_length",
            default=20,
            type=int,
            help="The maximum total input sequence length after tokenization. Sequences longer "
            "than this will be truncated, sequences shorter will be padded.",
        )
        parser.add_argument(
            "--data_dir",
            type=str,
            required=True,
            help="The input data dir. Should contain train.source, train.target, val.source, val.target, test.source, test.target",
        )
        parser.add_argument("--freeze_encoder", action="store_true")
        parser.add_argument("--freeze_embeds", action="store_true")
        parser.add_argument("--sortish_sampler", action="store_true", default=False)
        parser.add_argument("--logger_name", type=str, choices=["default", "wandb", "wandb_shared"], default="default")
        parser.add_argument("--n_train", type=int, default=-1, required=False, help="# examples. -1 means use all.")
        parser.add_argument("--n_val", type=int, default=500, required=False, help="# examples. -1 means use all.")
        parser.add_argument("--n_test", type=int, default=-1, required=False, help="# examples. -1 means use all.")
        parser.add_argument(
            "--task", type=str, default="summarization", required=False, help="# examples. -1 means use all."
        )
        parser.add_argument("--label_smoothing", type=float, default=0.0, required=False)
        parser.add_argument("--src_lang", type=str, default="", required=False)
        parser.add_argument("--tgt_lang", type=str, default="", required=False)
        parser.add_argument(
            "--early_stopping_patience",
            type=int,
            default=-1,
            required=False,
            help="-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So val_check_interval will effect it.",
        )
        return parser


class TranslationModule(SummarizationModule):
    mode = "translation"
    loss_names = ["loss"]
    metric_names = ["bleu"]
    val_metric = "bleu"

    def __init__(self, hparams, **kwargs):
        super().__init__(hparams, **kwargs)
        self.dataset_kwargs["src_lang"] = hparams.src_lang
        self.dataset_kwargs["tgt_lang"] = hparams.tgt_lang

    def calc_generative_metrics(self, preds, target) -> dict:
        return calculate_bleu_score(preds, target)


def main(args, model=None) -> SummarizationModule:
    Path(args.output_dir).mkdir(exist_ok=True)
    if len(os.listdir(args.output_dir)) > 3 and args.do_train:
        raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
    if model is None:
        if args.task == "summarization":
            model: SummarizationModule = SummarizationModule(args)
        else:
            model: SummarizationModule = TranslationModule(args)

    dataset = Path(args.data_dir).name
    if (
        args.logger_name == "default"
        or args.fast_dev_run
        or str(args.output_dir).startswith("/tmp")
        or str(args.output_dir).startswith("/var")
    ):
        logger = True  # don't pollute wandb logs unnecessarily
    elif args.logger_name == "wandb":
        from pytorch_lightning.loggers import WandbLogger

        project = os.environ.get("WANDB_PROJECT", dataset)
        logger = WandbLogger(name=model.output_dir.name, project=project)

    elif args.logger_name == "wandb_shared":
        from pytorch_lightning.loggers import WandbLogger

        logger = WandbLogger(name=model.output_dir.name, project=f"hf_{dataset}")

    if args.early_stopping_patience >= 0:
        es_callback = get_early_stopping_callback(model.val_metric, args.early_stopping_patience)
    else:
        es_callback = False
    trainer: pl.Trainer = generic_train(
        model,
        args,
        logging_callback=Seq2SeqLoggingCallback(),
        checkpoint_callback=get_checkpoint_callback(args.output_dir, model.val_metric),
        early_stopping_callback=es_callback,
        logger=logger,
        # TODO: early stopping callback seems messed up
    )
    pickle_save(model.hparams, model.output_dir / "hparams.pkl")
    if not args.do_predict:
        return model

    model.hparams.test_checkpoint = ""
    checkpoints = list(sorted(glob.glob(os.path.join(args.output_dir, "*.ckpt"), recursive=True)))
    if checkpoints:
        model.hparams.test_checkpoint = checkpoints[-1]
        trainer.resume_from_checkpoint = checkpoints[-1]
    trainer.logger.log_hyperparams(model.hparams)

    # test() without a model tests using the best checkpoint automatically
    trainer.test()
    return model


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser = pl.Trainer.add_argparse_args(parser)
    parser = SummarizationModule.add_model_specific_args(parser, os.getcwd())

    args = parser.parse_args()
    main(args)