Spaces:
Running
Running
| # 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) | |
| 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() | |
| 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) | |
| 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() | |
| def compute_loss(self, model, inputs, *args, **kwargs): | |
| return super().compute_loss(model, inputs, *args, **kwargs) | |
| 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") | |