# coding=utf-8 # Copyright 2024 the LlamaFactory team. # # 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 from dataclasses import dataclass from typing import Any, Dict, Literal, Optional, Sequence import fire import torch from torch.utils.data import DataLoader from tqdm import tqdm from transformers import DataCollatorForLanguageModeling, DataCollatorForSeq2Seq from llamafactory.data import get_dataset from llamafactory.extras.constants import IGNORE_INDEX from llamafactory.hparams import get_train_args from llamafactory.model import load_model, load_tokenizer @dataclass class PairwiseDataCollatorWithPadding(DataCollatorForSeq2Seq): r""" Data collator for pairwise data. """ train_on_prompt: bool = False def __call__(self, features: Sequence[Dict[str, Any]]) -> Dict[str, torch.Tensor]: r""" Pads batched data to the longest sequence in the batch. We generate 2 * n examples where the first n examples represent chosen examples and the last n examples represent rejected examples. """ chosen_features = [] for feature in features: prompt_len, answer_len = len(feature["prompt_ids"]), len(feature["chosen_ids"]) input_ids = feature["prompt_ids"] + feature["chosen_ids"] attention_mask = [1] * (prompt_len + answer_len) labels = input_ids if self.train_on_prompt else [IGNORE_INDEX] * prompt_len + feature["chosen_ids"] chosen_features.append({"input_ids": input_ids, "attention_mask": attention_mask, "labels": labels}) return super().__call__(chosen_features) def cal_ppl( model_name_or_path: str, save_name: str, batch_size: int = 4, stage: Literal["pt", "sft", "rm"] = "sft", dataset: str = "alpaca_en", dataset_dir: str = "data", template: str = "default", cutoff_len: int = 1024, max_samples: Optional[int] = None, train_on_prompt: bool = False, ): r""" Calculates the ppl on the dataset of the pre-trained models. Usage: python cal_ppl.py --model_name_or_path path_to_model --save_name ppl.json """ model_args, data_args, training_args, finetuning_args, _ = get_train_args( dict( stage=stage, model_name_or_path=model_name_or_path, dataset=dataset, dataset_dir=dataset_dir, template=template, cutoff_len=cutoff_len, max_samples=max_samples, train_on_prompt=train_on_prompt, output_dir="dummy_dir", overwrite_cache=True, ) ) tokenizer_module = load_tokenizer(model_args) tokenizer = tokenizer_module["tokenizer"] trainset = get_dataset(model_args, data_args, training_args, stage, **tokenizer_module) model = load_model(tokenizer, model_args, finetuning_args, is_trainable=False) if stage == "pt": data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) elif stage == "sft": data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX) elif stage == "rm": data_collator = PairwiseDataCollatorWithPadding( tokenizer=tokenizer, label_pad_token_id=IGNORE_INDEX, train_on_prompt=train_on_prompt ) else: raise NotImplementedError dataloader = DataLoader(trainset, batch_size, shuffle=False, collate_fn=data_collator, pin_memory=True) criterion = torch.nn.CrossEntropyLoss(reduction="none") total_ppl = 0 perplexities = [] batch: Dict[str, "torch.Tensor"] with torch.no_grad(): for batch in tqdm(dataloader): batch = batch.to(model.device) outputs = model(**batch) shift_logits: "torch.Tensor" = outputs["logits"][..., :-1, :] shift_labels: "torch.Tensor" = batch["labels"][..., 1:] loss_mask = shift_labels != IGNORE_INDEX flatten_logits = shift_logits.contiguous().view(shift_labels.size(0) * shift_labels.size(1), -1) flatten_labels = shift_labels.contiguous().view(-1) token_logps: "torch.Tensor" = criterion(flatten_logits, flatten_labels) token_logps = token_logps.contiguous().view(shift_logits.size(0), -1) sentence_logps = (token_logps * loss_mask).sum(-1) / loss_mask.sum(-1) total_ppl += sentence_logps.exp().sum().item() perplexities.extend(sentence_logps.exp().tolist()) with open(save_name, "w", encoding="utf-8") as f: json.dump(perplexities, f, indent=2) print("Average perplexity is {:.2f}".format(total_ppl / len(perplexities))) print("Perplexities have been saved at {}.".format(save_name)) if __name__ == "__main__": fire.Fire(cal_ppl)