seq2seq_trainer.py

from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.file_utils import is_torch_tpu_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode


logger = logging.get_logger(__name__)

arg_to_scheduler = {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    "constant": get_constant_schedule,
    "constant_w_warmup": get_constant_schedule_with_warmup,
}


class Seq2SeqTrainer(Trainer):
    def __init__(self, config=None, data_args=None, *args, **kwargs):
        super().__init__(*args, **kwargs)

        if config is None:
            assert isinstance(
                self.model, PreTrainedModel
            ), f"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is {self.model.__class__}"
            self.config = self._actual_model(self.model).config
        else:
            self.config = config

        self.data_args = data_args
        self.vocab_size = self.config.tgt_vocab_size if isinstance(self.config, FSMTConfig) else self.config.vocab_size

        if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
            assert (
                self.config.pad_token_id is not None
            ), "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss calculation or doing label smoothing."

        if self.config.pad_token_id is None and self.config.eos_token_id is not None:
            logger.warn(
                f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for padding.."
            )

        if self.args.label_smoothing == 0:
            self.loss_fn = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id)
        else:
            # dynamically import label_smoothed_nll_loss
            from utils import label_smoothed_nll_loss

            self.loss_fn = label_smoothed_nll_loss

    def create_optimizer_and_scheduler(self, num_training_steps: int):
        """
        Setup the optimizer and the learning rate scheduler.

        We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the
        Trainer's init through :obj:`optimizers`, or subclass and override this method in a subclass.
        """
        if self.optimizer is None:
            no_decay = ["bias", "LayerNorm.weight"]
            optimizer_grouped_parameters = [
                {
                    "params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay)],
                    "weight_decay": self.args.weight_decay,
                },
                {
                    "params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay)],
                    "weight_decay": 0.0,
                },
            ]
            if self.args.adafactor:
                self.optimizer = Adafactor(
                    optimizer_grouped_parameters,
                    lr=self.args.learning_rate,
                    scale_parameter=False,
                    relative_step=False,
                )

            else:
                self.optimizer = AdamW(
                    optimizer_grouped_parameters, lr=self.args.learning_rate, eps=self.args.adam_epsilon
                )

        if self.lr_scheduler is None:
            self.lr_scheduler = self._get_lr_scheduler(num_training_steps)
        else:  # ignoring --lr_scheduler
            logger.warn("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.")

    def _get_lr_scheduler(self, num_training_steps):
        schedule_func = arg_to_scheduler[self.args.lr_scheduler]
        if self.args.lr_scheduler == "constant":
            scheduler = schedule_func(self.optimizer)
        elif self.args.lr_scheduler == "constant_w_warmup":
            scheduler = schedule_func(self.optimizer, num_warmup_steps=self.args.warmup_steps)
        else:
            scheduler = schedule_func(
                self.optimizer, num_warmup_steps=self.args.warmup_steps, num_training_steps=num_training_steps
            )
        return scheduler

    def _get_train_sampler(self) -> Optional[torch.utils.data.sampler.Sampler]:
        if isinstance(self.train_dataset, torch.utils.data.IterableDataset):
            return None
        elif is_torch_tpu_available():
            return get_tpu_sampler(self.train_dataset)
        else:
            if self.args.sortish_sampler:
                self.train_dataset.make_sortish_sampler(
                    self.args.per_device_train_batch_size,
                    distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED),
                )

            return (
                RandomSampler(self.train_dataset)
                if self.args.local_rank == -1
                else DistributedSampler(self.train_dataset)
            )

    def _compute_loss(self, model, inputs, labels):
        if self.args.label_smoothing == 0:
            if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                # force training to ignore pad token
                logits = model(**inputs, use_cache=False)[0]
                loss = self.loss_fn(logits.view(-1, logits.shape[-1]), labels.view(-1))
            else:
                # compute usual loss via models
                loss, logits = model(**inputs, labels=labels, use_cache=False)[:2]
        else:
            # compute label smoothed loss
            logits = model(**inputs, use_cache=False)[0]
            lprobs = torch.nn.functional.log_softmax(logits, dim=-1)
            loss, _ = self.loss_fn(lprobs, labels, self.args.label_smoothing, ignore_index=self.config.pad_token_id)
        return loss, logits

    def compute_loss(self, model, inputs):
        labels = inputs.pop("labels")
        loss, _ = self._compute_loss(model, inputs, labels)
        return loss

    def prediction_step(
        self,
        model: nn.Module,
        inputs: Dict[str, Union[torch.Tensor, Any]],
        prediction_loss_only: bool,
        ignore_keys: Optional[List[str]] = None,
    ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
        """
        Perform an evaluation step on :obj:`model` using obj:`inputs`.

        Subclass and override to inject custom behavior.

        Args:
            model (:obj:`nn.Module`):
                The model to evaluate.
            inputs (:obj:`Dict[str, Union[torch.Tensor, Any]]`):
                The inputs and targets of the model.

                The dictionary will be unpacked before being fed to the model. Most models expect the targets under the
                argument :obj:`labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (:obj:`bool`):
                Whether or not to return the loss only.

        Return:
            Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
            A tuple with the loss, logits and labels (each being optional).
        """
        inputs = self._prepare_inputs(inputs)

        gen_kwargs = {
            "max_length": self.data_args.val_max_target_length
            if self.data_args is not None
            else self.config.max_length,
            "num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
        }

        if self.args.predict_with_generate and not self.args.prediction_loss_only:
            generated_tokens = self.model.generate(
                inputs["input_ids"],
                attention_mask=inputs["attention_mask"],
                **gen_kwargs,
            )
            # in case the batch is shorter than max length, the output should be padded
            if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_kwargs["max_length"])

        labels = inputs.pop("labels")
        with torch.no_grad():
            # compute loss on predict data
            loss, logits = self._compute_loss(model, inputs, labels)

        loss = loss.mean().detach()
        if self.args.prediction_loss_only:
            return (loss, None, None)

        logits = generated_tokens if self.args.predict_with_generate else logits

        if labels.shape[-1] < gen_kwargs["max_length"]:
            labels = self._pad_tensors_to_max_len(labels, gen_kwargs["max_length"])

        return (loss, logits, labels)

    def _pad_tensors_to_max_len(self, tensor, max_length):
        # If PAD token is not defined at least EOS token has to be defined
        pad_token_id = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

        if pad_token_id is None:
            raise ValueError(
                f"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be padded to `max_length`={max_length}"
            )

        padded_tensor = pad_token_id * torch.ones(
            (tensor.shape[0], max_length), dtype=tensor.dtype, device=tensor.device
        )
        padded_tensor[:, : tensor.shape[-1]] = tensor
        return padded_tensor