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"""data handling specific to DPO"""
import inspect
import logging
from functools import partial
from pathlib import Path
from typing import Any, List
import yaml
from datasets import DatasetDict, concatenate_datasets, load_dataset, load_from_disk
from axolotl.common.const import DEFAULT_DATASET_PREPARED_PATH
from axolotl.prompt_strategies.dpo import load as load_dpo
from axolotl.prompt_strategies.orpo import load as load_orpo
from axolotl.utils.data.utils import md5
from axolotl.utils.dict import DictDefault
from axolotl.utils.distributed import is_main_process, zero_first
from axolotl.utils.models import load_tokenizer
LOG = logging.getLogger("axolotl")
def _get_path(ds_hash, cfg):
prepared_ds_path = (
Path(cfg.dataset_prepared_path) / ds_hash
if cfg.dataset_prepared_path
else Path(DEFAULT_DATASET_PREPARED_PATH) / ds_hash
)
return prepared_ds_path
def _load_preprocessed_ds(cfg, sub_cfg):
ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
prepared_ds_path = _get_path(ds_hash, cfg)
dataset = None
# pylint: disable=duplicate-code
if (
cfg.dataset_prepared_path
and any(prepared_ds_path.glob("*"))
and not cfg.is_preprocess
):
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
dataset = load_from_disk(str(prepared_ds_path))
return dataset
def _save_preprocessed_ds(cfg, sub_cfg, dataset):
ds_hash = md5(yaml.dump(sub_cfg, Dumper=yaml.Dumper))
prepared_ds_path = _get_path(ds_hash, cfg)
if cfg.is_preprocess and is_main_process():
LOG.info(f"Loading prepared dataset from disk at {prepared_ds_path}...")
dataset.save_to_disk(str(prepared_ds_path))
def load_prepare_dpo_datasets(cfg):
def load_split(dataset_cfgs, _cfg):
split_datasets: List[Any] = []
for i, ds_cfg in enumerate(dataset_cfgs):
if ds_cfg["ds_type"] == "json":
for data_file in ds_cfg["data_files"]:
data_files = {ds_cfg["split"]: data_file}
ds = load_dataset( # pylint: disable=invalid-name
"json",
data_files=data_files,
split=ds_cfg["split"],
)
split_datasets.insert(i, ds)
else:
ds = load_dataset( # pylint: disable=invalid-name
ds_cfg["path"],
split=ds_cfg["split"],
)
split_datasets.insert(i, ds)
tokenizer = None
for i, data_set in enumerate(split_datasets):
_type = dataset_cfgs[i]["type"]
if _type:
if isinstance(_type, DictDefault):
_type = "user_defined.default"
if _cfg.rl == "orpo":
ds_transform_fn = load_orpo(_type, _cfg, dataset_idx=i)
else:
ds_transform_fn = load_dpo(_type, _cfg, dataset_idx=i)
sig = inspect.signature(ds_transform_fn)
if "tokenizer" in sig.parameters:
if not tokenizer:
tokenizer = load_tokenizer(_cfg)
ds_transform_fn = partial(ds_transform_fn, tokenizer=tokenizer)
data_set = data_set.map(
ds_transform_fn,
desc="Mapping RL Dataset",
)
if isinstance(data_set, DatasetDict):
data_set = data_set["train"]
split_datasets[i] = data_set
else:
# If no `type` is provided, assume the dataset is already in the expected format with
# "prompt", "chosen" and "rejected" already preprocessed
split_datasets[i] = data_set
return concatenate_datasets(split_datasets)
with zero_first(is_main_process()):
train_is_preprocessed = False
eval_is_preprocessed = False
if train_dataset := _load_preprocessed_ds(cfg, cfg.datasets):
train_is_preprocessed = True
else:
train_dataset = load_split(cfg.datasets, cfg)
eval_dataset = None
if cfg.test_datasets:
if eval_dataset := _load_preprocessed_ds(cfg, cfg.test_datasets):
eval_is_preprocessed = True
else:
eval_dataset = load_split(cfg.test_datasets, cfg)
if not eval_dataset:
eval_dataset = None
if not train_is_preprocessed:
_save_preprocessed_ds(cfg, cfg.datasets, train_dataset)
if eval_dataset and not eval_is_preprocessed:
_save_preprocessed_ds(cfg, cfg.test_datasets, eval_dataset)
return train_dataset, eval_dataset
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