image-match/python/py/Lib/site-packages/transformers/trainer_seq2seq.py

# Copyright 2020 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import contextlib
from collections.abc import Callable
from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Optional, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .generation.configuration_utils import GenerationConfig
from .integrations.deepspeed import is_deepspeed_zero3_enabled
from .integrations.fsdp import is_fsdp_managed_module
from .trainer import Trainer
from .utils import is_datasets_available, logging


if torch.distributed.is_available():
    from torch.distributed.fsdp import FullyShardedDataParallel

if is_datasets_available():
    import datasets

if TYPE_CHECKING:
    from torch.utils.data import IterableDataset

    from .data.data_collator import DataCollator
    from .feature_extraction_utils import FeatureExtractionMixin
    from .image_processing_utils import BaseImageProcessor
    from .modeling_utils import PreTrainedModel
    from .processing_utils import ProcessorMixin
    from .tokenization_utils_base import PreTrainedTokenizerBase
    from .trainer_callback import TrainerCallback
    from .trainer_utils import EvalPrediction, PredictionOutput
    from .training_args import TrainingArguments


logger = logging.get_logger(__name__)


class Seq2SeqTrainer(Trainer):
    def __init__(
        self,
        model: Union["PreTrainedModel", nn.Module] | None = None,
        args: Optional["TrainingArguments"] = None,
        data_collator: Optional["DataCollator"] = None,
        train_dataset: Union[Dataset, "IterableDataset", "datasets.Dataset"] | None = None,
        eval_dataset: Dataset | dict[str, Dataset] | None = None,
        processing_class: Union[
            "PreTrainedTokenizerBase", "BaseImageProcessor", "FeatureExtractionMixin", "ProcessorMixin"
        ]
        | None = None,
        model_init: Callable[[], "PreTrainedModel"] | None = None,
        compute_loss_func: Callable | None = None,
        compute_metrics: Callable[["EvalPrediction"], dict] | None = None,
        callbacks: list["TrainerCallback"] | None = None,
        optimizers: tuple[torch.optim.Optimizer | None, torch.optim.lr_scheduler.LambdaLR | None] = (None, None),
        preprocess_logits_for_metrics: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = None,
    ):
        super().__init__(
            model=model,
            args=args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            processing_class=processing_class,
            model_init=model_init,
            compute_loss_func=compute_loss_func,
            compute_metrics=compute_metrics,
            callbacks=callbacks,
            optimizers=optimizers,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )

        # Override self.model.generation_config if a GenerationConfig is specified in args.
        # Priority: args.generation_config > model.generation_config > default GenerationConfig.
        if self.args.generation_config is not None:
            gen_config = self.load_generation_config(self.args.generation_config)
            self.model.generation_config = gen_config

    @staticmethod
    def load_generation_config(gen_config_arg: str | GenerationConfig) -> GenerationConfig:
        """
        Loads a `~generation.GenerationConfig` from the `Seq2SeqTrainingArguments.generation_config` arguments.

        Args:
            gen_config_arg (`str` or [`~generation.GenerationConfig]`):
                `Seq2SeqTrainingArguments.generation_config` argument.

        Returns:
            A `~generation.GenerationConfig`.
        """

        # GenerationConfig provided, nothing to do
        if isinstance(gen_config_arg, GenerationConfig):
            gen_config = deepcopy(gen_config_arg)
        else:
            # str or Path
            pretrained_model_name = Path(gen_config_arg) if isinstance(gen_config_arg, str) else gen_config_arg
            config_file_name = None

            # Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
            # This step is required in order to determine config_file_name
            if pretrained_model_name.is_file():
                config_file_name = pretrained_model_name.name
                pretrained_model_name = pretrained_model_name.parent
            # dir path
            elif pretrained_model_name.is_dir():
                pass
            # model id or URL
            else:
                pretrained_model_name = gen_config_arg

            gen_config = GenerationConfig.from_pretrained(pretrained_model_name, config_file_name)

        # Strict validation to fail early. `GenerationConfig.save_pretrained()`, run at the end of training, throws
        # an exception if there are warnings at validation time.
        try:
            gen_config.validate(strict=True)
        except ValueError as exc:
            raise ValueError(str(exc) + "\n\nFix these issues to train your model.")

        return gen_config

    def evaluate(
        self,
        eval_dataset: Dataset | None = None,
        ignore_keys: list[str] | None = None,
        metric_key_prefix: str = "eval",
        **gen_kwargs,
    ) -> dict[str, float]:
        """
        Run evaluation and returns metrics.

        The calling script will be responsible for providing a method to compute metrics, as they are task-dependent
        (pass it to the init `compute_metrics` argument).

        You can also subclass and override this method to inject custom behavior.

        Args:
            eval_dataset (`Dataset`, *optional*):
                Pass a dataset if you wish to override `self.eval_dataset`. If it is an [`~datasets.Dataset`], columns
                not accepted by the `model.forward()` method are automatically removed. It must implement the `__len__`
                method.
            ignore_keys (`list[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"eval"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "eval_bleu" if the prefix is `"eval"` (default)
            max_length (`int`, *optional*):
                The maximum target length to use when predicting with the generate method.
            num_beams (`int`, *optional*):
                Number of beams for beam search that will be used when predicting with the generate method. 1 means no
                beam search.
            gen_kwargs:
                Additional `generate` specific kwargs.

        Returns:
            A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The
            dictionary also contains the epoch number which comes from the training state.
        """

        gen_kwargs = gen_kwargs.copy()

        # Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the
        # training args
        if (
            gen_kwargs.get("max_length") is None
            and gen_kwargs.get("max_new_tokens") is None
            and self.args.generation_max_length is not None
        ):
            gen_kwargs["max_length"] = self.args.generation_max_length
        if gen_kwargs.get("num_beams") is None and self.args.generation_num_beams is not None:
            gen_kwargs["num_beams"] = self.args.generation_num_beams
        # We don't want to drop samples in general
        self.gather_function = self.accelerator.gather
        self._gen_kwargs = gen_kwargs
        return super().evaluate(eval_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)

    def predict(
        self,
        test_dataset: Dataset,
        ignore_keys: list[str] | None = None,
        metric_key_prefix: str = "test",
        **gen_kwargs,
    ) -> "PredictionOutput":
        """
        Run prediction and returns predictions and potential metrics.

        Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method
        will also return metrics, like in `evaluate()`.

        Args:
            test_dataset (`Dataset`):
                Dataset to run the predictions on. If it is a [`~datasets.Dataset`], columns not accepted by the
                `model.forward()` method are automatically removed. Has to implement the method `__len__`
            ignore_keys (`list[str]`, *optional*):
                A list of keys in the output of your model (if it is a dictionary) that should be ignored when
                gathering predictions.
            metric_key_prefix (`str`, *optional*, defaults to `"eval"`):
                An optional prefix to be used as the metrics key prefix. For example the metrics "bleu" will be named
                "eval_bleu" if the prefix is `"eval"` (default)
            max_length (`int`, *optional*):
                The maximum target length to use when predicting with the generate method.
            num_beams (`int`, *optional*):
                Number of beams for beam search that will be used when predicting with the generate method. 1 means no
                beam search.
            gen_kwargs:
                Additional `generate` specific kwargs.

        <Tip>

        If your predictions or labels have different sequence lengths (for instance because you're doing dynamic
        padding in a token classification task) the predictions will be padded (on the right) to allow for
        concatenation into one array. The padding index is -100.

        </Tip>

        Returns: *NamedTuple* A namedtuple with the following keys:

            - predictions (`np.ndarray`): The predictions on `test_dataset`.
            - label_ids (`np.ndarray`, *optional*): The labels (if the dataset contained some).
            - metrics (`dict[str, float]`, *optional*): The potential dictionary of metrics (if the dataset contained
              labels).
        """

        gen_kwargs = gen_kwargs.copy()

        # Use legacy argument setting if a) the option is not explicitly passed; and b) the argument is set in the
        # training args
        if (
            gen_kwargs.get("max_length") is None
            and gen_kwargs.get("max_new_tokens") is None
            and self.args.generation_max_length is not None
        ):
            gen_kwargs["max_length"] = self.args.generation_max_length
        if gen_kwargs.get("num_beams") is None and self.args.generation_num_beams is not None:
            gen_kwargs["num_beams"] = self.args.generation_num_beams
        self.gather_function = self.accelerator.gather
        self._gen_kwargs = gen_kwargs

        return super().predict(test_dataset, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix)

    def prediction_step(
        self,
        model: nn.Module,
        inputs: dict[str, torch.Tensor | Any],
        prediction_loss_only: bool,
        ignore_keys: list[str] | None = None,
        **gen_kwargs,
    ) -> tuple[float | None, torch.Tensor | None, torch.Tensor | None]:
        """
        Perform an evaluation step on `model` using `inputs`.

        Subclass and override to inject custom behavior.

        Args:
            model (`nn.Module`):
                The model to evaluate.
            inputs (`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 `labels`. Check your model's documentation for all accepted arguments.
            prediction_loss_only (`bool`):
                Whether or not to return the loss only.
            gen_kwargs:
                Additional `generate` specific kwargs.

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

        if not self.args.predict_with_generate or prediction_loss_only:
            return super().prediction_step(
                model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys
            )

        has_labels = "labels" in inputs
        inputs = self._prepare_inputs(inputs)

        # Priority (handled in generate):
        # non-`None` gen_kwargs > model.generation_config > default GenerationConfig()
        if len(gen_kwargs) == 0 and hasattr(self, "_gen_kwargs"):
            gen_kwargs = self._gen_kwargs.copy()
        if "num_beams" in gen_kwargs and gen_kwargs["num_beams"] is None:
            gen_kwargs.pop("num_beams")
        if "max_length" in gen_kwargs and gen_kwargs["max_length"] is None:
            gen_kwargs.pop("max_length")

        default_synced_gpus = is_deepspeed_zero3_enabled() or is_fsdp_managed_module(self.model)
        gen_kwargs["synced_gpus"] = gen_kwargs.get("synced_gpus", default_synced_gpus)

        generation_inputs = inputs.copy()
        # If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
        # (otherwise, it would continue generating from the padded `decoder_input_ids`)
        if (
            "labels" in generation_inputs
            and "decoder_input_ids" in generation_inputs
            and generation_inputs["labels"].shape == generation_inputs["decoder_input_ids"].shape
        ):
            generation_inputs = {
                k: v for k, v in inputs.items() if k not in ("decoder_input_ids", "decoder_attention_mask")
            }

        summon_full_params_context = (
            FullyShardedDataParallel.summon_full_params(self.model)
            if torch.distributed.is_available() and isinstance(self.model, FullyShardedDataParallel)
            else contextlib.nullcontext()
        )

        with summon_full_params_context:
            generated_tokens = self.model.generate(**generation_inputs, **gen_kwargs)

        # Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
        # TODO: remove this hack when the legacy code that initializes generation_config from a model config is
        # removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
        if self.model.generation_config._from_model_config:
            self.model.generation_config._from_model_config = False

        # Retrieves GenerationConfig from model.generation_config
        # Update with defaults because earlier the generation config used to be init
        # with default values. Now we init it with `None` and keep defaults for BC
        gen_config = self.model.generation_config
        default_gen_config = gen_config._get_default_generation_params()
        gen_config.update(**default_gen_config, defaults_only=True)
        # in case the batch is shorter than max length, the output should be padded
        if generated_tokens.shape[-1] < gen_config.max_length:
            generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_config.max_length)
        elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
            generated_tokens = self._pad_tensors_to_max_len(generated_tokens, gen_config.max_new_tokens + 1)

        with torch.no_grad():
            if has_labels:
                with self.compute_loss_context_manager():
                    outputs = model(**inputs)
                if self.label_smoother is not None:
                    loss = self.label_smoother(outputs, inputs["labels"]).detach().mean()
                else:
                    loss = (outputs["loss"] if isinstance(outputs, dict) else outputs[0]).detach().mean()
            else:
                loss = None

        if self.args.prediction_loss_only:
            return loss, None, None

        if has_labels:
            labels = inputs["labels"]
            if labels.shape[-1] < gen_config.max_length:
                labels = self._pad_tensors_to_max_len(labels, gen_config.max_length)
            elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
                labels = self._pad_tensors_to_max_len(labels, gen_config.max_new_tokens + 1)
        else:
            labels = None

        return loss, generated_tokens, labels

    def _pad_tensors_to_max_len(self, tensor, max_length):
        if self.processing_class is not None and hasattr(self.processing_class, "pad_token_id"):
            # If PAD token is not defined at least EOS token has to be defined
            pad_token_id = (
                self.processing_class.pad_token_id
                if self.processing_class.pad_token_id is not None
                else self.processing_class.eos_token_id
            )
        else:
            if getattr(self.model.config, "pad_token_id", None) is not None:
                pad_token_id = self.model.config.pad_token_id
            else:
                raise ValueError("Pad_token_id must be set in the configuration of the model, in order to pad tensors")

        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