finetune.py

import argparse
import glob
import logging
import os
import time
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 torch.utils.data import DataLoader

from callbacks import Seq2SeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from lightning_base import BaseTransformer, add_generic_args, generic_train
from transformers import MBartTokenizer, T5ForConditionalGeneration
from transformers.modeling_bart import shift_tokens_right
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):
        if hparams.sortish_sampler and hparams.gpus > 1:
            hparams.replace_sampler_ddp = False
        elif hparams.max_tokens_per_batch is not None:
            if hparams.gpus > 1:
                raise NotImplementedError("Dynamic Batch size does not work for multi-gpu training")
            if hparams.sortish_sampler:
                raise ValueError("--sortish_sampler and --max_tokens_per_batch may not be used simultaneously")

        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.model_type = self.config.model_type
        self.vocab_size = self.config.tgt_vocab_size if self.model_type == "fsmt" else self.config.vocab_size

        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"
        if self.hparams.eval_max_gen_length is not None:
            self.eval_max_length = self.hparams.eval_max_gen_length
        else:
            self.eval_max_length = self.model.config.max_length
        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."""
        if self.model_type == "t5":
            freeze_params(self.model.shared)
            for d in [self.model.encoder, self.model.decoder]:
                freeze_params(d.embed_tokens)
        elif self.model_type == "fsmt":
            for d in [self.model.model.encoder, self.model.model.decoder]:
                freeze_params(d.embed_positions)
                freeze_params(d.embed_tokens)
        else:
            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)

    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.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()
        logs["bs"] = batch["input_ids"].shape[0]
        logs["src_pad_tok"] = batch["input_ids"].eq(self.pad).sum()
        logs["src_pad_frac"] = batch["input_ids"].eq(self.pad).float().mean()
        # TODO(SS): make a wandb summary metric for this
        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()

        # parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
        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,
            max_length=self.eval_max_length,
        )
        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)

        if self.hparams.sortish_sampler and type_path != "test":
            sampler = dataset.make_sortish_sampler(batch_size, distributed=self.hparams.gpus > 1)
            return DataLoader(
                dataset,
                batch_size=batch_size,
                collate_fn=dataset.collate_fn,
                shuffle=False,
                num_workers=self.num_workers,
                sampler=sampler,
            )

        elif self.hparams.max_tokens_per_batch is not None and type_path != "test":
            batch_sampler = dataset.make_dynamic_sampler(
                self.hparams.max_tokens_per_batch, distributed=self.hparams.gpus > 1
            )
            return DataLoader(
                dataset,
                batch_sampler=batch_sampler,
                collate_fn=dataset.collate_fn,
                # shuffle=False,
                num_workers=self.num_workers,
                # batch_size=None,
            )
        else:
            return DataLoader(
                dataset,
                batch_size=batch_size,
                collate_fn=dataset.collate_fn,
                shuffle=shuffle,
                num_workers=self.num_workers,
                sampler=None,
            )

    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("--max_tokens_per_batch", type=int, default=None)
        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("--eval_max_gen_length", type=int, default=None, help="never generate more than n tokens")
        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)
    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)