mirror of
https://github.com/hiyouga/LLaMA-Factory.git
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1fee69f874
* fix llamapro script * change year Former-commit-id: e2dc5b952aa22835d5220ba624f44676138b65ac
112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
# Copyright 2025 the LlamaFactory team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from functools import partial
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from typing import TYPE_CHECKING, Callable, Literal, Optional, Tuple
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from .processors.feedback import preprocess_feedback_dataset
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from .processors.pairwise import preprocess_pairwise_dataset, print_pairwise_dataset_example
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from .processors.pretrain import preprocess_pretrain_dataset, print_pretrain_dataset_example
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from .processors.supervised import (
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preprocess_packed_supervised_dataset,
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preprocess_supervised_dataset,
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print_supervised_dataset_example,
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)
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from .processors.unsupervised import preprocess_unsupervised_dataset, print_unsupervised_dataset_example
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if TYPE_CHECKING:
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from transformers import PreTrainedTokenizer, ProcessorMixin
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from ..hparams import DataArguments
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from .template import Template
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def get_preprocess_and_print_func(
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data_args: "DataArguments",
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stage: Literal["pt", "sft", "rm", "ppo", "kto"],
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template: "Template",
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tokenizer: "PreTrainedTokenizer",
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processor: Optional["ProcessorMixin"],
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do_generate: bool = False,
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) -> Tuple[Callable, Callable]:
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if stage == "pt":
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preprocess_func = partial(
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preprocess_pretrain_dataset,
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tokenizer=tokenizer,
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data_args=data_args,
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)
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print_function = partial(print_pretrain_dataset_example, tokenizer=tokenizer)
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elif stage == "sft" and not do_generate:
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if data_args.packing:
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if data_args.neat_packing: # hack datasets to have int32 attention mask
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from datasets.arrow_writer import OptimizedTypedSequence, TypedSequence
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def __init__(self, data, **kwargs):
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return TypedSequence.__init__(
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self,
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data,
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type=kwargs.pop("type", None),
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try_type=kwargs.pop("try_type", None),
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optimized_int_type=kwargs.pop("optimized_int_type", None),
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)
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OptimizedTypedSequence.__init__ = __init__
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preprocess_func = partial(
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preprocess_packed_supervised_dataset,
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template=template,
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tokenizer=tokenizer,
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processor=processor,
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data_args=data_args,
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)
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else:
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preprocess_func = partial(
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preprocess_supervised_dataset,
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template=template,
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tokenizer=tokenizer,
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processor=processor,
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data_args=data_args,
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)
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print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
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elif stage == "rm":
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preprocess_func = partial(
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preprocess_pairwise_dataset,
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template=template,
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tokenizer=tokenizer,
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processor=processor,
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data_args=data_args,
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)
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print_function = partial(print_pairwise_dataset_example, tokenizer=tokenizer)
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elif stage == "kto":
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preprocess_func = partial(
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preprocess_feedback_dataset,
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template=template,
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tokenizer=tokenizer,
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processor=processor,
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data_args=data_args,
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)
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print_function = partial(print_supervised_dataset_example, tokenizer=tokenizer)
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else:
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preprocess_func = partial(
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preprocess_unsupervised_dataset,
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template=template,
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tokenizer=tokenizer,
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processor=processor,
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data_args=data_args,
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)
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print_function = partial(print_unsupervised_dataset_example, tokenizer=tokenizer)
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return preprocess_func, print_function
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