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# Copyright 2025 the LlamaFactory team.
#
# 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.
import os
from abc import abstractmethod
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Optional, Union
from ..extras import logging
from .data_utils import Role
if TYPE_CHECKING:
from datasets import Dataset, IterableDataset
from transformers import Seq2SeqTrainingArguments
from ..hparams import DataArguments
from .mm_plugin import AudioInput, ImageInput, VideoInput
from .parser import DatasetAttr
MediaType = Union[ImageInput, VideoInput, AudioInput]
logger = logging.get_logger(__name__)
@dataclass
class DatasetConverter:
dataset_attr: "DatasetAttr"
data_args: "DataArguments"
def _find_medias(self, medias: Union["MediaType", list["MediaType"], None]) -> Optional[list["MediaType"]]:
r"""Optionally concatenate media path to media dir when loading from local disk."""
if medias is None:
return None
elif not isinstance(medias, list):
medias = [medias]
elif len(medias) == 0:
return None
else:
medias = medias[:]
if self.dataset_attr.load_from in ["script", "file"] and isinstance(medias[0], str):
for i in range(len(medias)):
if os.path.isfile(os.path.join(self.data_args.media_dir, medias[i])):
medias[i] = os.path.join(self.data_args.media_dir, medias[i])
else:
logger.warning_rank0_once(f"Media {medias[i]} does not exist in `media_dir`. Use original path.")
return medias
@abstractmethod
def __call__(self, example: dict[str, Any]) -> dict[str, Any]:
r"""Convert a single example in the dataset to the standard format."""
...
@dataclass
class AlpacaDatasetConverter(DatasetConverter):
def __call__(self, example: dict[str, Any]) -> dict[str, Any]:
prompt = []
if self.dataset_attr.history and isinstance(example[self.dataset_attr.history], list):
for old_prompt, old_response in example[self.dataset_attr.history]:
prompt.append({"role": Role.USER.value, "content": old_prompt})
prompt.append({"role": Role.ASSISTANT.value, "content": old_response})
query = []
if self.dataset_attr.prompt and example[self.dataset_attr.prompt]:
query.append(example[self.dataset_attr.prompt])
if self.dataset_attr.query and example[self.dataset_attr.query]:
query.append(example[self.dataset_attr.query])
prompt.append({"role": Role.USER.value, "content": "\n".join(query)}) # "prompt\nquery"
if self.dataset_attr.kto_tag and isinstance(example[self.dataset_attr.kto_tag], bool): # kto example
response = [{"role": Role.ASSISTANT.value, "content": example[self.dataset_attr.response]}]
if example[self.dataset_attr.kto_tag]:
response = response + [{"role": Role.ASSISTANT.value, "content": ""}]
else:
response = [{"role": Role.ASSISTANT.value, "content": ""}] + response
elif (
self.dataset_attr.ranking
and isinstance(example[self.dataset_attr.chosen], str)
and isinstance(example[self.dataset_attr.rejected], str)
): # pairwise example
response = [
{"role": Role.ASSISTANT.value, "content": example[self.dataset_attr.chosen]},
{"role": Role.ASSISTANT.value, "content": example[self.dataset_attr.rejected]},
]
elif self.dataset_attr.response and isinstance(example[self.dataset_attr.response], str): # normal example
response = [{"role": Role.ASSISTANT.value, "content": example[self.dataset_attr.response]}]
else: # unsupervised
response = []
output = {
"_prompt": prompt,
"_response": response,
"_system": example[self.dataset_attr.system] if self.dataset_attr.system else "",
"_tools": example[self.dataset_attr.tools] if self.dataset_attr.tools else "",
"_images": self._find_medias(example[self.dataset_attr.images]) if self.dataset_attr.images else None,
"_videos": self._find_medias(example[self.dataset_attr.videos]) if self.dataset_attr.videos else None,
"_audios": self._find_medias(example[self.dataset_attr.audios]) if self.dataset_attr.audios else None,
}
return output
@dataclass
class SharegptDatasetConverter(DatasetConverter):
def __call__(self, example: dict[str, Any]) -> dict[str, Any]:
tag_mapping = {
self.dataset_attr.user_tag: Role.USER.value,
self.dataset_attr.assistant_tag: Role.ASSISTANT.value,
self.dataset_attr.observation_tag: Role.OBSERVATION.value,
self.dataset_attr.function_tag: Role.FUNCTION.value,
self.dataset_attr.system_tag: Role.SYSTEM.value,
}
odd_tags = (self.dataset_attr.user_tag, self.dataset_attr.observation_tag)
even_tags = (self.dataset_attr.assistant_tag, self.dataset_attr.function_tag)
accept_tags = (odd_tags, even_tags)
messages = example[self.dataset_attr.messages]
if (
self.dataset_attr.system_tag
and len(messages) != 0
and messages[0][self.dataset_attr.role_tag] == self.dataset_attr.system_tag
):
system = messages[0][self.dataset_attr.content_tag]
messages = messages[1:]
else:
system = example[self.dataset_attr.system] if self.dataset_attr.system else ""
aligned_messages = []
broken_data = False
for turn_idx, message in enumerate(messages):
if message[self.dataset_attr.role_tag] not in accept_tags[turn_idx % 2]:
logger.warning_rank0(f"Invalid role tag in {messages}.")
broken_data = True
break
aligned_messages.append(
{
"role": tag_mapping[message[self.dataset_attr.role_tag]],
"content": message[self.dataset_attr.content_tag],
}
)
if (not self.dataset_attr.ranking and len(aligned_messages) % 2 != 0) or (
self.dataset_attr.ranking and len(aligned_messages) % 2 == 0
):
logger.warning_rank0(f"Invalid message count in {messages}.")
broken_data = True
if broken_data:
logger.warning_rank0("Skipping this abnormal example.")
prompt, response = [], []
elif self.dataset_attr.kto_tag and isinstance(example[self.dataset_attr.kto_tag], bool): # kto example
prompt = aligned_messages[:-1]
response = aligned_messages[-1:]
if example[self.dataset_attr.kto_tag]:
response = response + [{"role": Role.ASSISTANT.value, "content": ""}]
else:
response = [{"role": Role.ASSISTANT.value, "content": ""}] + response
elif (
self.dataset_attr.ranking
and isinstance(example[self.dataset_attr.chosen], dict)
and isinstance(example[self.dataset_attr.rejected], dict)
): # pairwise example
chosen = example[self.dataset_attr.chosen]
rejected = example[self.dataset_attr.rejected]
if (
chosen[self.dataset_attr.role_tag] not in accept_tags[-1]
or rejected[self.dataset_attr.role_tag] not in accept_tags[-1]
):
logger.warning_rank0(f"Invalid role tag in {[chosen, rejected]}.")
broken_data = True
prompt = aligned_messages
response = [
{
"role": tag_mapping[chosen[self.dataset_attr.role_tag]],
"content": chosen[self.dataset_attr.content_tag],
},
{
"role": tag_mapping[rejected[self.dataset_attr.role_tag]],
"content": rejected[self.dataset_attr.content_tag],
},
]
else: # normal example
prompt = aligned_messages[:-1]
response = aligned_messages[-1:]
output = {
"_prompt": prompt,
"_response": response,
"_system": system,
"_tools": example[self.dataset_attr.tools] if self.dataset_attr.tools else "",
"_images": self._find_medias(example[self.dataset_attr.images]) if self.dataset_attr.images else None,
"_videos": self._find_medias(example[self.dataset_attr.videos]) if self.dataset_attr.videos else None,
"_audios": self._find_medias(example[self.dataset_attr.audios]) if self.dataset_attr.audios else None,
}
return output
DATASET_CONVERTERS = {
"alpaca": AlpacaDatasetConverter,
"sharegpt": SharegptDatasetConverter,
}
def register_dataset_converter(name: str, dataset_converter: type["DatasetConverter"]) -> None:
r"""Register a new dataset converter."""
if name in DATASET_CONVERTERS:
raise ValueError(f"Dataset converter {name} already exists.")
DATASET_CONVERTERS[name] = dataset_converter
def get_dataset_converter(name: str, dataset_attr: "DatasetAttr", data_args: "DataArguments") -> "DatasetConverter":
r"""Get a dataset converter."""
if name not in DATASET_CONVERTERS:
raise ValueError(f"Dataset converter {name} not found.")
return DATASET_CONVERTERS[name](dataset_attr, data_args)
def align_dataset(
dataset: Union["Dataset", "IterableDataset"],
dataset_attr: "DatasetAttr",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
) -> Union["Dataset", "IterableDataset"]:
r"""Align the dataset to a specific format.
Aligned dataset:
_prompt: [{"role": "user", "content": "..."}] * (2T - 1)
_response: [{"role": "assistant", "content": "..."}] * N (N > 1 for ranking dataset)
_system: "..."
_tools: "..."
_images: []
_videos: []
_audios: []
"""
column_names = list(next(iter(dataset)).keys())
kwargs = {}
if not data_args.streaming:
kwargs = dict(
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=(not data_args.overwrite_cache) or (training_args.local_process_index != 0),
desc="Converting format of dataset",
)
dataset_converter = get_dataset_converter(dataset_attr.formatting, dataset_attr, data_args)
return dataset.map(
dataset_converter,
batched=False,
remove_columns=column_names,
**kwargs,
)
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