lightning_base.py

import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeq2SeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
    get_linear_schedule_with_warmup,
)


logger = logging.getLogger(__name__)


MODEL_MODES = {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeq2SeqLM,
    "translation": AutoModelForSeq2SeqLM,
}


class BaseTransformer(pl.LightningModule):
    def __init__(
        self,
        hparams: argparse.Namespace,
        num_labels=None,
        mode="base",
        config=None,
        tokenizer=None,
        model=None,
        **config_kwargs
    ):
        """Initialize a model, tokenizer and config."""
        super().__init__()
        # TODO: move to self.save_hyperparameters()
        # self.save_hyperparameters()
        # can also expand arguments into trainer signature for easier reading

        self.hparams = hparams
        self.step_count = 0
        self.tfmr_ckpts = {}
        self.output_dir = Path(self.hparams.output_dir)
        cache_dir = self.hparams.cache_dir if self.hparams.cache_dir else None
        if config is None:
            self.config = AutoConfig.from_pretrained(
                self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path,
                **({"num_labels": num_labels} if num_labels is not None else {}),
                cache_dir=cache_dir,
                **config_kwargs,
            )
        else:
            self.config: PretrainedConfig = config

        extra_model_params = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
        for p in extra_model_params:
            if getattr(self.hparams, p, None):
                assert hasattr(self.config, p), f"model config doesn't have a `{p}` attribute"
                setattr(self.config, p, getattr(self.hparams, p))

        if tokenizer is None:
            self.tokenizer = AutoTokenizer.from_pretrained(
                self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path,
                cache_dir=cache_dir,
            )
        else:
            self.tokenizer: PreTrainedTokenizer = tokenizer
        self.model_type = MODEL_MODES[mode]
        if model is None:
            self.model = self.model_type.from_pretrained(
                self.hparams.model_name_or_path,
                from_tf=bool(".ckpt" in self.hparams.model_name_or_path),
                config=self.config,
                cache_dir=cache_dir,
            )
        else:
            self.model = model

    def load_hf_checkpoint(self, *args, **kwargs):
        self.model = self.model_type.from_pretrained(*args, **kwargs)

    def configure_optimizers(self):
        "Prepare optimizer and schedule (linear warmup and decay)"
        model = self.model
        no_decay = ["bias", "LayerNorm.weight"]
        optimizer_grouped_parameters = [
            {
                "params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
                "weight_decay": self.hparams.weight_decay,
            },
            {
                "params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
                "weight_decay": 0.0,
            },
        ]
        optimizer = AdamW(optimizer_grouped_parameters, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
        self.opt = optimizer

        scheduler = get_linear_schedule_with_warmup(
            self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.total_steps
        )
        scheduler = {"scheduler": scheduler, "interval": "step", "frequency": 1}
        return [optimizer], [scheduler]

    def test_step(self, batch, batch_nb):
        return self.validation_step(batch, batch_nb)

    def test_epoch_end(self, outputs):
        return self.validation_end(outputs)

    def setup(self, step):
        train_batch_size = self.hparams.train_batch_size
        dataloader = self.get_dataloader("train", train_batch_size)
        self.train_loader = dataloader
        self.total_steps = (
            (len(dataloader.dataset) // (train_batch_size * max(1, self.hparams.gpus)))
            // self.hparams.accumulate_grad_batches
            * float(self.hparams.max_epochs)
        )

    def train_dataloader(self):
        return self.train_loader

    def val_dataloader(self):
        return self.get_dataloader("dev", self.hparams.eval_batch_size)

    def test_dataloader(self):
        return self.get_dataloader("test", self.hparams.eval_batch_size)

    def _feature_file(self, mode):
        return os.path.join(
            self.hparams.data_dir,
            "cached_{}_{}_{}".format(
                mode,
                list(filter(None, self.hparams.model_name_or_path.split("/"))).pop(),
                str(self.hparams.max_seq_length),
            ),
        )

    @pl.utilities.rank_zero_only
    def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None:
        save_path = self.output_dir.joinpath("best_tfmr")
        save_path.mkdir(exist_ok=True)
        self.model.config.save_step = self.step_count
        self.model.save_pretrained(save_path)
        self.tokenizer.save_pretrained(save_path)
        self.tfmr_ckpts[self.step_count] = save_path

    @staticmethod
    def add_model_specific_args(parser, root_dir):
        parser.add_argument(
            "--model_name_or_path",
            default=None,
            type=str,
            required=True,
            help="Path to pretrained model or model identifier from huggingface.co/models",
        )
        parser.add_argument(
            "--config_name", default="", type=str, help="Pretrained config name or path if not the same as model_name"
        )
        parser.add_argument(
            "--tokenizer_name",
            default=None,
            type=str,
            help="Pretrained tokenizer name or path if not the same as model_name",
        )
        parser.add_argument(
            "--cache_dir",
            default="",
            type=str,
            help="Where do you want to store the pre-trained models downloaded from s3",
        )
        parser.add_argument(
            "--encoder_layerdrop",
            type=float,
            help="Encoder layer dropout probability (Optional). Goes into model.config",
        )
        parser.add_argument(
            "--decoder_layerdrop",
            type=float,
            help="Decoder layer dropout probability (Optional). Goes into model.config",
        )
        parser.add_argument(
            "--dropout", type=float, help="Dropout probability (Optional). Goes into model.config",
        )
        parser.add_argument(
            "--attention_dropout", type=float, help="Attention dropout probability (Optional). Goes into model.config",
        )
        parser.add_argument("--learning_rate", default=5e-5, type=float, help="The initial learning rate for Adam.")
        parser.add_argument("--weight_decay", default=0.0, type=float, help="Weight decay if we apply some.")
        parser.add_argument("--adam_epsilon", default=1e-8, type=float, help="Epsilon for Adam optimizer.")
        parser.add_argument("--warmup_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
        parser.add_argument("--num_workers", default=4, type=int, help="kwarg passed to DataLoader")
        parser.add_argument("--num_train_epochs", dest="max_epochs", default=3, type=int)
        parser.add_argument("--train_batch_size", default=32, type=int)
        parser.add_argument("--eval_batch_size", default=32, type=int)


class LoggingCallback(pl.Callback):
    def on_batch_end(self, trainer, pl_module):
        lrs = {f"lr_group_{i}": lr for i, lr in enumerate(self.lr_scheduler.get_lr())}
        pl_module.logger.log_metrics(lrs)

    def on_validation_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
        rank_zero_info("***** Validation results *****")
        metrics = trainer.callback_metrics
        # Log results
        for key in sorted(metrics):
            if key not in ["log", "progress_bar"]:
                rank_zero_info("{} = {}\n".format(key, str(metrics[key])))

    def on_test_end(self, trainer: pl.Trainer, pl_module: pl.LightningModule):
        rank_zero_info("***** Test results *****")
        metrics = trainer.callback_metrics
        # Log and save results to file
        output_test_results_file = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
        with open(output_test_results_file, "w") as writer:
            for key in sorted(metrics):
                if key not in ["log", "progress_bar"]:
                    rank_zero_info("{} = {}\n".format(key, str(metrics[key])))
                    writer.write("{} = {}\n".format(key, str(metrics[key])))


def add_generic_args(parser, root_dir) -> None:
    #  TODO(SS): allow all pl args? parser = pl.Trainer.add_argparse_args(parser)
    parser.add_argument(
        "--output_dir",
        default=None,
        type=str,
        required=True,
        help="The output directory where the model predictions and checkpoints will be written.",
    )

    parser.add_argument(
        "--fp16",
        action="store_true",
        help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit",
    )

    parser.add_argument(
        "--fp16_opt_level",
        type=str,
        default="O2",
        help="For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
        "See details at https://nvidia.github.io/apex/amp.html",
    )
    parser.add_argument("--n_tpu_cores", dest="tpu_cores", type=int, default=0)
    parser.add_argument("--max_grad_norm", dest="gradient_clip_val", default=1.0, type=float, help="Max gradient norm")
    parser.add_argument("--do_train", action="store_true", help="Whether to run training.")
    parser.add_argument("--do_predict", action="store_true", help="Whether to run predictions on the test set.")
    parser.add_argument(
        "--gradient_accumulation_steps",
        dest="accumulate_grad_batches",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )

    parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")


def generic_train(
    model: BaseTransformer,
    args: argparse.Namespace,
    early_stopping_callback=False,
    logger=True,  # can pass WandbLogger() here
    extra_callbacks=[],
    checkpoint_callback=None,
    logging_callback=None,
    **extra_train_kwargs
):
    pl.seed_everything(args.seed)

    # init model
    odir = Path(model.hparams.output_dir)
    odir.mkdir(exist_ok=True)

    # add custom checkpoints
    if checkpoint_callback is None:
        checkpoint_callback = pl.callbacks.ModelCheckpoint(
            filepath=args.output_dir, prefix="checkpoint", monitor="val_loss", mode="min", save_top_k=1
        )
    if logging_callback is None:
        logging_callback = LoggingCallback()

    train_params = {}

    # TODO: remove with PyTorch 1.6 since pl uses native amp
    if args.fp16:
        train_params["precision"] = 16
        train_params["amp_level"] = args.fp16_opt_level

    if args.gpus > 1:
        train_params["distributed_backend"] = "ddp"

    trainer = pl.Trainer.from_argparse_args(
        args,
        weights_summary=None,
        callbacks=[logging_callback] + extra_callbacks,
        logger=logger,
        checkpoint_callback=checkpoint_callback,
        early_stop_callback=early_stopping_callback,
        **train_params,
    )

    if args.do_train:
        trainer.fit(model)

    return trainer