# Copyright 2025 HuggingFace Inc. and the LlamaFactory team. # # This code is inspired by the HuggingFace's transformers library. # https://github.com/huggingface/transformers/blob/v4.40.0/src/transformers/trainer_seq2seq.py # # 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 json import os from types import MethodType from typing import TYPE_CHECKING, Any, Optional, Union import numpy as np import torch from transformers import Seq2SeqTrainer from typing_extensions import override from ...extras import logging from ...extras.constants import IGNORE_INDEX from ...extras.packages import is_transformers_version_greater_than from ..callbacks import SaveProcessorCallback from ..trainer_utils import create_custom_optimizer, create_custom_scheduler if TYPE_CHECKING: from torch.utils.data import Dataset from transformers import PreTrainedTokenizer, ProcessorMixin from transformers.trainer import PredictionOutput from ...hparams import FinetuningArguments logger = logging.get_logger(__name__) class CustomSeq2SeqTrainer(Seq2SeqTrainer): r"""Inherits Seq2SeqTrainer to compute generative metrics such as BLEU and ROUGE.""" def __init__( self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], gen_kwargs: Optional[dict[str, Any]] = None, **kwargs, ) -> None: if is_transformers_version_greater_than("4.46"): kwargs["processing_class"] = kwargs.pop("tokenizer") else: self.processing_class: PreTrainedTokenizer = kwargs.get("tokenizer") super().__init__(**kwargs) if processor is not None: # avoid wrong loss under gradient accumulation # https://github.com/huggingface/transformers/pull/36044#issuecomment-2746657112 self.model_accepts_loss_kwargs = False self.finetuning_args = finetuning_args if gen_kwargs is not None: # https://github.com/huggingface/transformers/blob/v4.45.0/src/transformers/trainer_seq2seq.py#L287 self._gen_kwargs = gen_kwargs if processor is not None: self.add_callback(SaveProcessorCallback(processor)) if finetuning_args.use_badam: from badam import BAdamCallback, clip_grad_norm_old_version # type: ignore self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator) self.add_callback(BAdamCallback) @override def create_optimizer(self) -> "torch.optim.Optimizer": if self.optimizer is None: self.optimizer = create_custom_optimizer(self.model, self.args, self.finetuning_args) return super().create_optimizer() @override def create_scheduler( self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None ) -> "torch.optim.lr_scheduler.LRScheduler": create_custom_scheduler(self.args, num_training_steps, optimizer) return super().create_scheduler(num_training_steps, optimizer) @override def _get_train_sampler(self) -> Optional["torch.utils.data.Sampler"]: if self.finetuning_args.disable_shuffling: return torch.utils.data.SequentialSampler(self.train_dataset) return super()._get_train_sampler() @override def compute_loss(self, model, inputs, *args, **kwargs): return super().compute_loss(model, inputs, *args, **kwargs) @override def prediction_step( self, model: "torch.nn.Module", inputs: dict[str, Union["torch.Tensor", Any]], prediction_loss_only: bool, ignore_keys: Optional[list[str]] = None, **gen_kwargs, ) -> tuple[Optional[float], Optional["torch.Tensor"], Optional["torch.Tensor"]]: r"""Remove the prompt part in the generated tokens. Subclass and override to inject custom behavior. """ if self.args.predict_with_generate: # do not pass labels to model when generate labels = inputs.pop("labels", None) else: labels = inputs.get("labels") loss, generated_tokens, _ = super().prediction_step( model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys, **gen_kwargs ) if generated_tokens is not None and self.args.predict_with_generate: generated_tokens[:, : inputs["input_ids"].size(-1)] = self.processing_class.pad_token_id generated_tokens = generated_tokens.contiguous() return loss, generated_tokens, labels def save_predictions( self, dataset: "Dataset", predict_results: "PredictionOutput", skip_special_tokens: bool = True ) -> None: r"""Save model predictions to `output_dir`. A custom behavior that not contained in Seq2SeqTrainer. """ if not self.is_world_process_zero(): return output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl") logger.info_rank0(f"Saving prediction results to {output_prediction_file}") labels = np.where( predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.processing_class.pad_token_id ) preds = np.where( predict_results.predictions != IGNORE_INDEX, predict_results.predictions, self.processing_class.pad_token_id, ) for i in range(len(preds)): pad_len = np.nonzero(preds[i] != self.processing_class.pad_token_id)[0] if len(pad_len): # move pad token to last preds[i] = np.concatenate((preds[i][pad_len[0] :], preds[i][: pad_len[0]]), axis=-1) decoded_inputs = self.processing_class.batch_decode(dataset["input_ids"], skip_special_tokens=False) decoded_preds = self.processing_class.batch_decode(preds, skip_special_tokens=skip_special_tokens) decoded_labels = self.processing_class.batch_decode(labels, skip_special_tokens=skip_special_tokens) with open(output_prediction_file, "w", encoding="utf-8") as f: for text, pred, label in zip(decoded_inputs, decoded_preds, decoded_labels): f.write(json.dumps({"prompt": text, "predict": pred, "label": label}, ensure_ascii=False) + "\n")