Source code for flash.text.seq2seq.translation.model

# Copyright The PyTorch Lightning team.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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from typing import Any, Dict, Optional, Union

from torchmetrics import BLEUScore

from flash.core.utilities.types import LOSS_FN_TYPE, LR_SCHEDULER_TYPE, METRICS_TYPE, OPTIMIZER_TYPE
from flash.text.seq2seq.core.model import Seq2SeqTask


class TranslationTask(Seq2SeqTask):
    """The ``TranslationTask`` is a :class:`~flash.Task` for Seq2Seq text translation. For more details, see
    :ref:`translation`.

    You can change the backbone to any translation model from `HuggingFace/transformers
    <https://huggingface.co/models?filter=pytorch&pipeline_tag=translation>`__ using the ``backbone`` argument.

    Args:
        backbone: backbone model to use for the task.
        max_source_length: The maximum length to pad / truncate input sequences to.
        max_target_length: The maximum length to pad / truncate target sequences to.
        padding: The type of padding to apply. One of: "longest" or ``True``, "max_length", "do_not_pad" or
            ``False``.
        loss_fn: Loss function for training.
        optimizer: Optimizer to use for training.
        lr_scheduler: The LR scheduler to use during training.
        metrics: Metrics to compute for training and evaluation. Defauls to calculating the BLEU metric.
            Changing this argument currently has no effect.
        learning_rate: Learning rate to use for training, defaults to `1e-5`
        num_beams: Number of beams to use in validation when generating predictions. Defaults to `4`
        n_gram: Maximum n_grams to use in metric calculation. Defaults to `4`
        smooth: Apply smoothing in BLEU calculation. Defaults to `True`
        enable_ort: Enable Torch ONNX Runtime Optimization: https://onnxruntime.ai/docs/#onnx-runtime-for-training
    """

    def __init__(
        self,
        backbone: str = "t5-small",
        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
        max_source_length: int = 128,
        max_target_length: int = 128,
        padding: Union[str, bool] = "max_length",
        loss_fn: LOSS_FN_TYPE = None,
        optimizer: OPTIMIZER_TYPE = "Adam",
        lr_scheduler: LR_SCHEDULER_TYPE = None,
        metrics: METRICS_TYPE = None,
        learning_rate: Optional[float] = None,
        num_beams: Optional[int] = 4,
        n_gram: int = 4,
        smooth: bool = True,
        enable_ort: bool = False,
    ):
        self.save_hyperparameters()
        super().__init__(
            backbone=backbone,
            tokenizer_kwargs=tokenizer_kwargs,
            max_source_length=max_source_length,
            max_target_length=max_target_length,
            padding=padding,
            loss_fn=loss_fn,
            optimizer=optimizer,
            lr_scheduler=lr_scheduler,
            metrics=metrics,
            learning_rate=learning_rate,
            num_beams=num_beams,
            enable_ort=enable_ort,
        )
        self.bleu = BLEUScore(
            n_gram=n_gram,
            smooth=smooth,
        )

    @property
    def task(self) -> str:
        return "translation"

    def compute_metrics(self, generated_tokens, batch, prefix):
        reference_corpus = self.decode(batch["labels"])
        # wrap targets in list as score expects a list of potential references
        reference_corpus = [[reference] for reference in reference_corpus]

        translate_corpus = self.decode(generated_tokens)
        translate_corpus = list(translate_corpus)

        result = self.bleu(translate_corpus, reference_corpus)
        self.log(f"{prefix}_bleu_score", result, on_step=False, on_epoch=True, prog_bar=True)