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# Copyright 2024 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 json
import os
from dataclasses import dataclass
from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional
from ..extras.constants import DATA_CONFIG
from ..extras.misc import use_modelscope
if TYPE_CHECKING:
from ..hparams import DataArguments
@dataclass
class DatasetAttr:
r"""
Dataset attributes.
"""
# basic configs
load_from: Literal["hf_hub", "ms_hub", "script", "file"]
dataset_name: str
formatting: Literal["alpaca", "sharegpt"] = "alpaca"
ranking: bool = False
# extra configs
subset: Optional[str] = None
folder: Optional[str] = None
num_samples: Optional[int] = None
# common columns
system: Optional[str] = None
tools: Optional[str] = None
images: Optional[str] = None
# rlhf columns
chosen: Optional[str] = None
rejected: Optional[str] = None
kto_tag: Optional[str] = None
# alpaca columns
prompt: Optional[str] = "instruction"
query: Optional[str] = "input"
response: Optional[str] = "output"
history: Optional[str] = None
# sharegpt columns
messages: Optional[str] = "conversations"
# sharegpt tags
role_tag: Optional[str] = "from"
content_tag: Optional[str] = "value"
user_tag: Optional[str] = "human"
assistant_tag: Optional[str] = "gpt"
observation_tag: Optional[str] = "observation"
function_tag: Optional[str] = "function_call"
system_tag: Optional[str] = "system"
def __repr__(self) -> str:
return self.dataset_name
def set_attr(self, key: str, obj: Dict[str, Any], default: Optional[Any] = None) -> None:
setattr(self, key, obj.get(key, default))
def get_dataset_list(data_args: "DataArguments") -> List["DatasetAttr"]:
if data_args.dataset is not None:
dataset_names = [ds.strip() for ds in data_args.dataset.split(",")]
else:
dataset_names = []
if data_args.dataset_dir == "ONLINE":
dataset_info = None
else:
try:
with open(os.path.join(data_args.dataset_dir, DATA_CONFIG), "r") as f:
dataset_info = json.load(f)
except Exception as err:
if len(dataset_names) != 0:
raise ValueError(
"Cannot open {} due to {}.".format(os.path.join(data_args.dataset_dir, DATA_CONFIG), str(err))
)
dataset_info = None
if data_args.interleave_probs is not None:
data_args.interleave_probs = [float(prob.strip()) for prob in data_args.interleave_probs.split(",")]
dataset_list: List[DatasetAttr] = []
for name in dataset_names:
if dataset_info is None:
load_from = "ms_hub" if use_modelscope() else "hf_hub"
dataset_attr = DatasetAttr(load_from, dataset_name=name)
dataset_list.append(dataset_attr)
continue
if name not in dataset_info:
raise ValueError("Undefined dataset {} in {}.".format(name, DATA_CONFIG))
has_hf_url = "hf_hub_url" in dataset_info[name]
has_ms_url = "ms_hub_url" in dataset_info[name]
if has_hf_url or has_ms_url:
if (use_modelscope() and has_ms_url) or (not has_hf_url):
dataset_attr = DatasetAttr("ms_hub", dataset_name=dataset_info[name]["ms_hub_url"])
else:
dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
elif "script_url" in dataset_info[name]:
dataset_attr = DatasetAttr("script", dataset_name=dataset_info[name]["script_url"])
else:
dataset_attr = DatasetAttr("file", dataset_name=dataset_info[name]["file_name"])
dataset_attr.set_attr("formatting", dataset_info[name], default="alpaca")
dataset_attr.set_attr("ranking", dataset_info[name], default=False)
dataset_attr.set_attr("subset", dataset_info[name])
dataset_attr.set_attr("folder", dataset_info[name])
dataset_attr.set_attr("num_samples", dataset_info[name])
if "columns" in dataset_info[name]:
column_names = ["system", "tools", "images", "chosen", "rejected", "kto_tag"]
if dataset_attr.formatting == "alpaca":
column_names.extend(["prompt", "query", "response", "history"])
else:
column_names.extend(["messages"])
for column_name in column_names:
dataset_attr.set_attr(column_name, dataset_info[name]["columns"])
if dataset_attr.formatting == "sharegpt" and "tags" in dataset_info[name]:
tag_names = (
"role_tag",
"content_tag",
"user_tag",
"assistant_tag",
"observation_tag",
"function_tag",
"system_tag",
)
for tag in tag_names:
dataset_attr.set_attr(tag, dataset_info[name]["tags"])
dataset_list.append(dataset_attr)
return dataset_list
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