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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.
from functools import partial
from typing import TYPE_CHECKING, Callable, Literal, Optional, Tuple
from .processors.feedback import preprocess_feedback_dataset
from .processors.pairwise import preprocess_pairwise_dataset, print_pairwise_dataset_example
from .processors.pretrain import preprocess_pretrain_dataset
from .processors.supervised import (
preprocess_packed_supervised_dataset,
preprocess_supervised_dataset,
print_supervised_dataset_example,
)
from .processors.unsupervised import preprocess_unsupervised_dataset, print_unsupervised_dataset_example
if TYPE_CHECKING:
from transformers import PreTrainedTokenizer, ProcessorMixin
from ..hparams import DataArguments
from .template import Template
def get_preprocess_and_print_func(
data_args: "DataArguments",
stage: Literal["pt", "sft", "rm", "ppo", "kto"],
template: "Template",
tokenizer: "PreTrainedTokenizer",
processor: Optional["ProcessorMixin"],
do_generate: bool = False,
) -> Tuple[Callable, Callable]:
if stage == "pt":
preprocess_func = partial(
preprocess_pretrain_dataset,
tokenizer=tokenizer,
data_args=data_args,
)
print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
elif stage == "sft" and not do_generate:
if data_args.packing:
if data_args.neat_packing: # hack datasets to have int32 attention mask
from datasets.arrow_writer import OptimizedTypedSequence, TypedSequence
def __init__(self, data, **kwargs):
return TypedSequence.__init__(
self,
data,
type=kwargs.pop("type", None),
try_type=kwargs.pop("try_type", None),
optimized_int_type=kwargs.pop("optimized_int_type", None),
)
OptimizedTypedSequence.__init__ = __init__
preprocess_func = partial(
preprocess_packed_supervised_dataset,
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
else:
preprocess_func = partial(
preprocess_supervised_dataset,
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
elif stage == "rm":
preprocess_func = partial(
preprocess_pairwise_dataset,
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
elif stage == "kto":
preprocess_func = partial(
preprocess_feedback_dataset,
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
else:
preprocess_func = partial(
preprocess_unsupervised_dataset,
template=template,
tokenizer=tokenizer,
processor=processor,
data_args=data_args,
)
print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
return preprocess_func, print_function
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