# Copyright 2024 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, Dict, List, Optional, Tuple, Union import numpy as np import torch from transformers import Seq2SeqTrainer from ...extras.constants import IGNORE_INDEX from ...extras.logging import get_logger from ..trainer_utils import convert_pissa_adapter, create_custom_optimzer, create_custom_scheduler if TYPE_CHECKING: from torch.utils.data import Dataset from transformers import ProcessorMixin from transformers.trainer import PredictionOutput from ...hparams import FinetuningArguments logger = 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"], **kwargs ) -> None: super().__init__(**kwargs) self.finetuning_args = finetuning_args self.processor = processor if finetuning_args.pissa_convert: self.save_model(os.path.join(self.args.output_dir, "pissa_init")) if finetuning_args.use_badam: from badam import clip_grad_norm_for_sparse_tensor self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_for_sparse_tensor, self.accelerator) def create_optimizer(self) -> "torch.optim.Optimizer": if self.optimizer is None: self.optimizer = create_custom_optimzer(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 _save(self, output_dir: Optional[str] = None, state_dict: Optional[Dict[str, "torch.Tensor"]] = None) -> None: super()._save(output_dir, state_dict) output_dir = output_dir if output_dir is not None else self.args.output_dir if self.finetuning_args.pissa_convert: convert_pissa_adapter(output_dir, state_dict, self.accelerator, self.model, self.args) if self.processor is not None: getattr(self.processor, "image_processor").save_pretrained(output_dir) 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, ) -> Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]: r""" Removes the prompt part in the generated tokens. Subclass and override to inject custom behavior. """ labels = inputs["labels"].detach().clone() if "labels" in inputs else None # backup labels if self.args.predict_with_generate: assert self.tokenizer.padding_side == "left", "This method only accepts left-padded tensor." prompt_len, label_len = inputs["input_ids"].size(-1), inputs["labels"].size(-1) if prompt_len > label_len: inputs["labels"] = self._pad_tensors_to_target_len(inputs["labels"], inputs["input_ids"]) if label_len > prompt_len: # truncate the labels instead of padding the inputs (llama2 fp16 compatibility) inputs["labels"] = inputs["labels"][:, :prompt_len] loss, generated_tokens, _ = super().prediction_step( # ignore the returned labels (may be truncated) model, inputs, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys ) if generated_tokens is not None and self.args.predict_with_generate: generated_tokens[:, :prompt_len] = self.tokenizer.pad_token_id generated_tokens = generated_tokens.contiguous() return loss, generated_tokens, labels def _pad_tensors_to_target_len(self, src_tensor: torch.Tensor, tgt_tensor: torch.Tensor) -> torch.Tensor: r""" Pads the tensor to the same length as the target tensor. """ assert self.tokenizer.pad_token_id is not None, "Pad token is required." padded_tensor = self.tokenizer.pad_token_id * torch.ones_like(tgt_tensor) padded_tensor[:, -src_tensor.shape[-1] :] = src_tensor # adopt left-padding return padded_tensor.contiguous() # in contiguous memory def save_predictions(self, dataset: "Dataset", predict_results: "PredictionOutput") -> None: r""" Saves 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(f"Saving prediction results to {output_prediction_file}") labels = np.where( predict_results.label_ids != IGNORE_INDEX, predict_results.label_ids, self.tokenizer.pad_token_id ) preds = np.where( predict_results.predictions != IGNORE_INDEX, predict_results.predictions, self.tokenizer.pad_token_id ) for i in range(len(preds)): pad_len = np.nonzero(preds[i] != self.tokenizer.pad_token_id)[0] if len(pad_len): preds[i] = np.concatenate( (preds[i][pad_len[0] :], preds[i][: pad_len[0]]), axis=-1 ) # move pad token to last decoded_inputs = self.tokenizer.batch_decode( dataset["input_ids"], skip_special_tokens=True, clean_up_tokenization_spaces=False ) decoded_labels = self.tokenizer.batch_decode( labels, skip_special_tokens=True, clean_up_tokenization_spaces=False ) decoded_preds = self.tokenizer.batch_decode(preds, skip_special_tokens=True, clean_up_tokenization_spaces=True) with open(output_prediction_file, "w", encoding="utf-8") as writer: res: List[str] = [] for text, label, pred in zip(decoded_inputs, decoded_labels, decoded_preds): res.append(json.dumps({"prompt": text, "label": label, "predict": pred}, ensure_ascii=False)) writer.write("\n".join(res))