File size: 3,518 Bytes
236083b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | # Copyright Lightning AI. Licensed under the Apache License 2.0, see LICENSE file.
import json
from dataclasses import dataclass, field
from pathlib import Path
import torch
from torch.utils.data import DataLoader
from litgpt.data import DataModule, SFTDataset, get_sft_collate_fn
from litgpt.data.alpaca import download_if_missing
from litgpt.prompts import PromptStyle
from litgpt.tokenizer import Tokenizer
_URL = "https://raw.githubusercontent.com/akoksal/LongForm/main/dataset"
@dataclass
class LongForm(DataModule):
"""LongForm data module for supervised finetuning."""
mask_prompt: bool = False
"""Whether to mask the prompt section from the label (with ``ignore_index``)."""
prompt_style: str | PromptStyle = "longform"
"""The style to apply to instruction prompts. See `litgpt.prompts` for a list of available styles."""
ignore_index: int = -100
"""The index to use for elements to be ignored in the label."""
seed: int = 42
"""The random seed for shuffling the dataset."""
num_workers: int = 4
"""How many DataLoader processes to use for loading."""
download_dir: Path = Path("./data/longform")
"""The directory in which the downloaded dataset gets saved."""
tokenizer: Tokenizer | None = field(default=None, init=False, repr=False)
batch_size: int = field(default=1, init=False, repr=False)
max_seq_length: int = field(default=-1, init=False, repr=False)
train_dataset: SFTDataset | None = field(default=None, init=False, repr=False)
test_dataset: SFTDataset | None = field(default=None, init=False, repr=False)
def __post_init__(self) -> None:
super().__init__()
if isinstance(self.prompt_style, str):
self.prompt_style = PromptStyle.from_name(self.prompt_style)
def connect(
self, tokenizer: Tokenizer | None = None, batch_size: int = 1, max_seq_length: int | None = None
) -> None:
self.tokenizer = tokenizer
self.batch_size = batch_size
self.max_seq_length = -1 if max_seq_length is None else max_seq_length
def prepare_data(self) -> None:
self.download_dir.mkdir(parents=True, exist_ok=True)
download_if_missing(self.download_dir / "train.json", f"{_URL}/train.json")
download_if_missing(self.download_dir / "val.json", f"{_URL}/val.json")
def train_dataloader(self):
return self._dataloader("train")
def val_dataloader(self):
return self._dataloader("val")
def _dataloader(self, split: str) -> DataLoader:
with open(self.download_dir / f"{split}.json", encoding="utf-8") as file:
data = json.load(file)
dataset = SFTDataset(
data=data,
tokenizer=self.tokenizer,
prompt_style=self.prompt_style,
max_seq_length=self.max_seq_length,
mask_prompt=self.mask_prompt,
ignore_index=self.ignore_index,
transform=_transform,
)
return DataLoader(
dataset=dataset,
batch_size=self.batch_size,
shuffle=(split == "train"),
generator=torch.Generator().manual_seed(self.seed),
num_workers=self.num_workers,
collate_fn=get_sft_collate_fn(max_seq_length=self.max_seq_length, ignore_index=self.ignore_index),
)
def _transform(item: dict) -> dict:
item["instruction"] = item.pop("input")
return item
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