wehub-resource-sync
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import torch
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from abc import ABC, abstractmethod
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from swift.trainers import Trainer, TrainingArguments
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class BaseLoss(ABC):
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"""Abstract base class for custom loss functions.
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This class provides a common interface for implementing custom loss functions
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that can be integrated with the ms-swift training framework. All custom loss
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implementations should inherit from this class and implement the __call__ method.
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Attributes:
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args (TrainingArguments): Training configuration and hyperparameters.
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trainer (Trainer): Reference to the trainer instance for accessing model
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and training state.
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"""
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def __init__(self, args: 'TrainingArguments', trainer: 'Trainer'):
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"""Initialize the loss function with training arguments and trainer.
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Args:
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args (TrainingArguments): Training configuration and hyperparameters.
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trainer (Trainer): Reference to the trainer instance.
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"""
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self.args = args
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self.trainer = trainer
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mro_class_names = [cls.__name__ for cls in trainer.__class__.__mro__]
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self.is_megatron = 'BaseMegatronTrainer' in mro_class_names
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@abstractmethod
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def __call__(self, outputs, labels, *, num_items_in_batch=None, loss_scale=None, **kwargs) -> torch.Tensor:
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"""Calculate the loss value.
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This method must be implemented by all subclasses to define the specific
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loss calculation logic.
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Args:
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outputs: Model outputs.
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labels: Ground truth labels or targets.
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num_items_in_batch (int, optional): Number of items (tokens) in the current batch,
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Defaults to None.
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loss_scale (float, optional): Scaling factor to apply to the loss value.
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Defaults to None.
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Returns:
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torch.Tensor: A scalar tensor representing the computed loss value.
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"""
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pass
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