finetune.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 pytorch_lightning as pl
import torch
from packaging import version
from torch.utils.data import DataLoader

from lightning_base import BaseTransformer, add_generic_args, generic_train
from transformers import MBartTokenizer, T5ForConditionalGeneration
from transformers.modeling_bart import shift_tokens_right


try:
    from .callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
    from .utils import (
        ROUGE_KEYS,
        LegacySeq2SeqDataset,
        Seq2SeqDataset,
        assert_all_frozen,
        calculate_bleu,
        calculate_rouge,
        flatten_list,
        freeze_params,
        get_git_info,
        label_smoothed_nll_loss,
        lmap,
        pickle_save,
        save_git_info,
        save_json,
        use_task_specific_params,
    )
except ImportError:
    from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
    from utils import (
        ROUGE_KEYS,
        LegacySeq2SeqDataset,
        Seq2SeqDataset,
        assert_all_frozen,
        calculate_bleu,
        calculate_rouge,
        flatten_list,
        freeze_params,
        get_git_info,
        label_smoothed_nll_loss,
        lmap,
        pickle_save,
        save_git_info,
        save_json,
        use_task_specific_params,
    )

logger = logging.getLogger(__name__)


class SummarizationModule(BaseTransformer):
    mode = "summarization"
    loss_names = ["loss"]
    metric_names = ROUGE_KEYS
    default_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  # default to config
        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
        self.dataset_class = (
            Seq2SeqDataset if hasattr(self.tokenizer, "prepare_seq2seq_batch") else LegacySeq2SeqDataset
        )
        self.eval_beams = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
        assert self.eval_beams >= 1, f"got self.eval_beams={self.eval_beams}. Need an integer > 1"
        self.val_metric = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric

    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
        src_ids, src_mask = batch["input_ids"], batch["attention_mask"]
        tgt_ids = batch["labels"]
        if isinstance(self.model, T5ForConditionalGeneration):
            decoder_input_ids = self.model._shift_right(tgt_ids)
        else:
            decoder_input_ids = shift_tokens_right(tgt_ids, pad_token_id)

        outputs = self(src_ids, attention_mask=src_mask, decoder_input_ids=decoder_input_ids, use_cache=False)
        lm_logits = outputs[0]
        if self.hparams.label_smoothing == 0:
            # Same behavior as modeling_bart.py, besides ignoring pad_token_id
            ce_loss_fct = torch.nn.CrossEntropyLoss(ignore_index=pad_token_id)

            assert lm_logits.shape[-1] == self.model.config.vocab_size
            loss = ce_loss_fct(lm_logits.view(-1, lm_logits.shape[-1]), tgt_ids.view(-1))
        else:
            lprobs = torch.nn.functional.log_softmax(lm_logits, dim=-1)
            loss, nll_loss = label_smoothed_nll_loss(
                lprobs, tgt_ids, 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["labels"].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"]
        generative_metrics = {
            k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ["gen_time", "gen_len"]
        }
        metric_val = (
            generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
        )
        metric_tensor: torch.FloatTensor = torch.tensor(metric_val).type_as(loss)
        generative_metrics.update({k: v.item() for k, v in losses.items()})
        losses.update(generative_metrics)
        all_metrics = {f"{prefix}_avg_{k}": x for k, x in losses.items()}
        all_metrics["step_count"] = self.step_count
        self.save_metrics(all_metrics, prefix)  # writes to self.metrics_save_path
        preds = flatten_list([x["preds"] for x in outputs])
        return {
            "log": all_metrics,
            "preds": preds,
            f"{prefix}_loss": loss,
            f"{prefix}_{self.val_metric}": metric_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,
            num_beams=self.eval_beams,
        )
        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["labels"])
        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)
        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=1024,
            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=56,
            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=142,  # 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=142,
            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("--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("--eval_beams", type=int, default=None, required=False)
        parser.add_argument(
            "--val_metric", type=str, default=None, required=False, choices=["bleu", "rouge2", "loss", None]
        )
        parser.add_argument("--save_top_k", type=int, default=1, required=False, help="How many checkpoints to save")
        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"]
    default_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(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 "summarization" in args.task:
            model: SummarizationModule = SummarizationModule(args)
        else:
            model: SummarizationModule = TranslationModule(args)
    if version.parse(torch.__version__) == version.parse("1.6") and args.fp16:
        warnings.warn("FP16 only seems to work with torch 1.5+apex")
    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

    lower_is_better = args.val_metric == "loss"
    trainer: pl.Trainer = generic_train(
        model,
        args,
        logging_callback=Seq2SeqLoggingCallback(),
        checkpoint_callback=get_checkpoint_callback(
            args.output_dir, model.val_metric, args.save_top_k, lower_is_better
        ),
        early_stopping_callback=es_callback,
        logger=logger,
    )
    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)