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# Copyright 2025 HuggingFace Inc. and the LlamaFactory team.
#
# This code is inspired by the HuggingFace's transformers library.
# https://github.com/huggingface/transformers/blob/v4.40.0/examples/pytorch/language-modeling/run_clm.py
#
# 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.
from dataclasses import asdict, dataclass, field
from typing import Any, Literal, Optional
@dataclass
class DataArguments:
r"""Arguments pertaining to what data we are going to input our model for training and evaluation."""
template: Optional[str] = field(
default=None,
metadata={"help": "Which template to use for constructing prompts in training and inference."},
)
dataset: Optional[str] = field(
default=None,
metadata={"help": "The name of dataset(s) to use for training. Use commas to separate multiple datasets."},
)
eval_dataset: Optional[str] = field(
default=None,
metadata={"help": "The name of dataset(s) to use for evaluation. Use commas to separate multiple datasets."},
)
dataset_dir: str = field(
default="data",
metadata={"help": "Path to the folder containing the datasets."},
)
media_dir: Optional[str] = field(
default=None,
metadata={"help": "Path to the folder containing the images, videos or audios. Defaults to `dataset_dir`."},
)
cutoff_len: int = field(
default=2048,
metadata={"help": "The cutoff length of the tokenized inputs in the dataset."},
)
train_on_prompt: bool = field(
default=False,
metadata={"help": "Whether or not to disable the mask on the prompt."},
)
mask_history: bool = field(
default=False,
metadata={"help": "Whether or not to mask the history and train on the last turn only."},
)
streaming: bool = field(
default=False,
metadata={"help": "Enable dataset streaming."},
)
buffer_size: int = field(
default=16384,
metadata={"help": "Size of the buffer to randomly sample examples from in dataset streaming."},
)
mix_strategy: Literal["concat", "interleave_under", "interleave_over"] = field(
default="concat",
metadata={"help": "Strategy to use in dataset mixing (concat/interleave) (undersampling/oversampling)."},
)
interleave_probs: Optional[str] = field(
default=None,
metadata={"help": "Probabilities to sample data from datasets. Use commas to separate multiple datasets."},
)
overwrite_cache: bool = field(
default=False,
metadata={"help": "Overwrite the cached training and evaluation sets."},
)
preprocessing_batch_size: int = field(
default=1000,
metadata={"help": "The number of examples in one group in pre-processing."},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the pre-processing."},
)
max_samples: Optional[int] = field(
default=None,
metadata={"help": "For debugging purposes, truncate the number of examples for each dataset."},
)
eval_num_beams: Optional[int] = field(
default=None,
metadata={"help": "Number of beams to use for evaluation. This argument will be passed to `model.generate`"},
)
ignore_pad_token_for_loss: bool = field(
default=True,
metadata={"help": "Whether or not to ignore the tokens corresponding to the pad label in loss computation."},
)
val_size: float = field(
default=0.0,
metadata={"help": "Size of the validation set, should be an integer or a float in range `[0,1)`."},
)
eval_on_each_dataset: bool = field(
default=False,
metadata={"help": "Whether or not to evaluate on each dataset separately."},
)
packing: Optional[bool] = field(
default=None,
metadata={"help": "Enable sequences packing in training. Will automatically enable in pre-training."},
)
neat_packing: bool = field(
default=False,
metadata={"help": "Enable sequence packing without cross-attention."},
)
tool_format: Optional[str] = field(
default=None,
metadata={"help": "Tool format to use for constructing function calling examples."},
)
tokenized_path: Optional[str] = field(
default=None,
metadata={
"help": (
"Path to save or load the tokenized datasets. "
"If tokenized_path not exists, it will save the tokenized datasets. "
"If tokenized_path exists, it will load the tokenized datasets."
)
},
)
def __post_init__(self):
def split_arg(arg):
if isinstance(arg, str):
return [item.strip() for item in arg.split(",")]
return arg
self.dataset = split_arg(self.dataset)
self.eval_dataset = split_arg(self.eval_dataset)
if self.media_dir is None:
self.media_dir = self.dataset_dir
if self.dataset is None and self.val_size > 1e-6:
raise ValueError("Cannot specify `val_size` if `dataset` is None.")
if self.eval_dataset is not None and self.val_size > 1e-6:
raise ValueError("Cannot specify `val_size` if `eval_dataset` is not None.")
if self.interleave_probs is not None:
if self.mix_strategy == "concat":
raise ValueError("`interleave_probs` is only valid for interleaved mixing.")
self.interleave_probs = list(map(float, split_arg(self.interleave_probs)))
if self.dataset is not None and len(self.dataset) != len(self.interleave_probs):
raise ValueError("The length of dataset and interleave probs should be identical.")
if self.eval_dataset is not None and len(self.eval_dataset) != len(self.interleave_probs):
raise ValueError("The length of eval dataset and interleave probs should be identical.")
if self.streaming and self.val_size > 1e-6 and self.val_size < 1:
raise ValueError("Streaming mode should have an integer val size.")
if self.streaming and self.max_samples is not None:
raise ValueError("`max_samples` is incompatible with `streaming`.")
if self.mask_history and self.train_on_prompt:
raise ValueError("`mask_history` is incompatible with `train_on_prompt`.")
if self.neat_packing:
self.packing = True
if self.packing:
self.cutoff_len -= 1 # avoid pad_to_multiple_of, needs improve
def to_dict(self) -> dict[str, Any]:
return asdict(self)
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