117 lines
4.6 KiB
Python
117 lines
4.6 KiB
Python
# coding=utf-8
|
|
# 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
|
|
|
|
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,
|
|
):
|
|
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)
|