trainer.py

from typing import Any, Dict, Union

import torch
from packaging import version
from torch import nn

from transformers import (
    Trainer,
    is_apex_available,
)

if is_apex_available():
    from apex import amp

if version.parse(torch.__version__) >= version.parse("1.6"):
    _is_native_amp_available = True
    from torch.cuda.amp import autocast


class CTCTrainer(Trainer):
    def training_step(self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) -> torch.Tensor:
        model.train()
        inputs = self._prepare_inputs(inputs)

        if self.use_amp:
            with autocast():
                loss = self.compute_loss(model, inputs)
        else:
            loss = self.compute_loss(model, inputs)

        if self.args.gradient_accumulation_steps > 1:
            loss = loss / self.args.gradient_accumulation_steps

        if self.use_amp:
            self.scaler.scale(loss).backward()
        elif self.use_apex:
            with amp.scale_loss(loss, self.optimizer) as scaled_loss:
                scaled_loss.backward()
        elif self.deepspeed:
            self.deepspeed.backward(loss)
        else:
            loss.backward()

        return loss.detach()