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