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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 os
from functools import partial
from typing import TYPE_CHECKING, Any, Dict, List, Union
from datasets import Features
from ..extras.logging import get_logger
from .data_utils import Role
if TYPE_CHECKING:
from datasets import Dataset, IterableDataset
from transformers import Seq2SeqTrainingArguments
from ..hparams import DataArguments
from .parser import DatasetAttr
logger = get_logger(__name__)
def _convert_images(images: List[Any], dataset_attr: "DatasetAttr", data_args: "DataArguments") -> List[Any]:
r"""
Optionally concatenates image path to dataset dir when loading from local disk.
"""
outputs = []
if dataset_attr.load_from in ["script", "file"]:
for image in images:
if isinstance(image, str) and os.path.isfile(os.path.join(data_args.dataset_dir, image)):
outputs.append(os.path.join(data_args.dataset_dir, image))
else:
outputs.append(image)
return outputs
def convert_alpaca(
examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
) -> Dict[str, List[Any]]:
r"""
Converts alpaca format dataset to the standard format.
"""
outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
for i in range(len(examples[dataset_attr.prompt])):
prompt = []
if dataset_attr.history and isinstance(examples[dataset_attr.history][i], list):
for old_prompt, old_response in examples[dataset_attr.history][i]:
prompt.append({"role": Role.USER.value, "content": old_prompt})
prompt.append({"role": Role.ASSISTANT.value, "content": old_response})
content = []
if dataset_attr.prompt and examples[dataset_attr.prompt][i]:
content.append(examples[dataset_attr.prompt][i])
if dataset_attr.query and examples[dataset_attr.query][i]:
content.append(examples[dataset_attr.query][i])
prompt.append({"role": Role.USER.value, "content": "\n".join(content)}) # "prompt\nquery"
if dataset_attr.kto_tag and isinstance(examples[dataset_attr.kto_tag][i], bool): # kto example
response = [{"role": Role.ASSISTANT.value, "content": examples[dataset_attr.response][i]}]
if examples[dataset_attr.kto_tag][i]:
response = response + [{"role": Role.ASSISTANT.value, "content": ""}]
else:
response = [{"role": Role.ASSISTANT.value, "content": ""}] + response
elif (
dataset_attr.ranking
and isinstance(examples[dataset_attr.chosen][i], str)
and isinstance(examples[dataset_attr.rejected][i], str)
): # pairwise example
response = [
{"role": Role.ASSISTANT.value, "content": examples[dataset_attr.chosen][i]},
{"role": Role.ASSISTANT.value, "content": examples[dataset_attr.rejected][i]},
]
elif dataset_attr.response and isinstance(examples[dataset_attr.response][i], str): # normal example
response = [{"role": Role.ASSISTANT.value, "content": examples[dataset_attr.response][i]}]
else: # unsupervised
response = []
outputs["prompt"].append(prompt)
outputs["response"].append(response)
outputs["system"].append(examples[dataset_attr.system][i] if dataset_attr.system else "")
outputs["tools"].append(examples[dataset_attr.tools][i] if dataset_attr.tools else "")
outputs["images"].append(convert_images(examples[dataset_attr.images][i]) if dataset_attr.images else [])
return outputs
def convert_sharegpt(
examples: Dict[str, List[Any]], dataset_attr: "DatasetAttr", data_args: "DataArguments"
) -> Dict[str, List[Any]]:
r"""
Converts sharegpt format dataset to the standard format.
"""
outputs = {"prompt": [], "response": [], "system": [], "tools": [], "images": []}
convert_images = partial(_convert_images, dataset_attr=dataset_attr, data_args=data_args)
tag_mapping = {
dataset_attr.user_tag: Role.USER.value,
dataset_attr.assistant_tag: Role.ASSISTANT.value,
dataset_attr.observation_tag: Role.OBSERVATION.value,
dataset_attr.function_tag: Role.FUNCTION.value,
dataset_attr.system_tag: Role.SYSTEM.value,
}
odd_tags = (dataset_attr.user_tag, dataset_attr.observation_tag)
even_tags = (dataset_attr.assistant_tag, dataset_attr.function_tag)
accept_tags = (odd_tags, even_tags)
for i, messages in enumerate(examples[dataset_attr.messages]):
if dataset_attr.system_tag and messages[0][dataset_attr.role_tag] == dataset_attr.system_tag:
system = messages[0][dataset_attr.content_tag]
messages = messages[1:]
else:
system = examples[dataset_attr.system][i] if dataset_attr.system else ""
if len(messages) == 0:
continue
aligned_messages = []
broken_data = False
for turn_idx, message in enumerate(messages):
if message[dataset_attr.role_tag] not in accept_tags[turn_idx % 2]:
logger.warning("Invalid role tag in {}.".format(messages))
broken_data = True
aligned_messages.append(
{"role": tag_mapping[message[dataset_attr.role_tag]], "content": message[dataset_attr.content_tag]}
)
if (not dataset_attr.ranking and len(aligned_messages) % 2 != 0) or (
dataset_attr.ranking and len(aligned_messages) % 2 == 0
):
logger.warning("Invalid message count in {}.".format(messages))
broken_data = True
if dataset_attr.kto_tag and isinstance(examples[dataset_attr.kto_tag][i], bool): # kto example
prompt = aligned_messages[:-1]
response = aligned_messages[-1:]
if examples[dataset_attr.kto_tag][i]:
response = response + [{"role": Role.ASSISTANT.value, "content": ""}]
else:
response = [{"role": Role.ASSISTANT.value, "content": ""}] + response
elif (
dataset_attr.ranking
and isinstance(examples[dataset_attr.chosen][i], dict)
and isinstance(examples[dataset_attr.rejected][i], dict)
): # pairwise example
chosen = examples[dataset_attr.chosen][i]
rejected = examples[dataset_attr.rejected][i]
if (
chosen[dataset_attr.role_tag] not in accept_tags[-1]
or rejected[dataset_attr.role_tag] not in accept_tags[-1]
):
logger.warning("Invalid role tag in {}.".format([chosen, rejected]))
broken_data = True
prompt = aligned_messages
response = [
{"role": tag_mapping[chosen[dataset_attr.role_tag]], "content": chosen[dataset_attr.content_tag]},
{"role": tag_mapping[rejected[dataset_attr.role_tag]], "content": rejected[dataset_attr.content_tag]},
]
else: # normal example
prompt = aligned_messages[:-1]
response = aligned_messages[-1:]
if broken_data:
logger.warning("Skipping this abnormal example.")
continue
outputs["prompt"].append(prompt)
outputs["response"].append(response)
outputs["system"].append(system)
outputs["tools"].append(examples[dataset_attr.tools][i] if dataset_attr.tools else "")
outputs["images"].append(convert_images(examples[dataset_attr.images][i]) if dataset_attr.images else [])
return outputs
def align_dataset(
dataset: Union["Dataset", "IterableDataset"],
dataset_attr: "DatasetAttr",
data_args: "DataArguments",
training_args: "Seq2SeqTrainingArguments",
) -> Union["Dataset", "IterableDataset"]:
r"""
Aligned dataset:
prompt: [{"role": "user", "content": "..."}] * (2T - 1)
response: [{"role": "assistant", "content": "..."}] * N (N > 1 for ranking dataset)
system: "..."
tools: "...",
images: [],
"""
if dataset_attr.formatting == "alpaca":
convert_func = partial(convert_alpaca, dataset_attr=dataset_attr, data_args=data_args)
else:
convert_func = partial(convert_sharegpt, dataset_attr=dataset_attr, data_args=data_args)
column_names = list(next(iter(dataset)).keys())
features = Features.from_dict(
{
"prompt": [
{"role": {"dtype": "string", "_type": "Value"}, "content": {"dtype": "string", "_type": "Value"}}
],
"response": [
{"role": {"dtype": "string", "_type": "Value"}, "content": {"dtype": "string", "_type": "Value"}}
],
"system": {"dtype": "string", "_type": "Value"},
"tools": {"dtype": "string", "_type": "Value"},
"images": [{"_type": "Image"}],
}
)
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",
)
return dataset.map(
convert_func,
batched=True,
remove_columns=column_names,
features=features,
**kwargs,
)
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