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https://github.com/hiyouga/LLaMA-Factory.git
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ca7b65439d
Deprecate reserved_label_len arg Former-commit-id: 1771251ce3f6887b301dac10f3de7a253c5e5884
89 lines
3.3 KiB
Python
89 lines
3.3 KiB
Python
# Copyright 2024 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 enum import Enum, unique
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from typing import TYPE_CHECKING, Dict, List, Sequence, Set, Union
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from datasets import concatenate_datasets, interleave_datasets
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from ..extras.logging import get_logger
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if TYPE_CHECKING:
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from datasets import Dataset, IterableDataset
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from transformers import Seq2SeqTrainingArguments
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from ..hparams import DataArguments
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logger = get_logger(__name__)
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SLOTS = Sequence[Union[str, Set[str], Dict[str, str]]]
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@unique
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class Role(str, Enum):
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USER = "user"
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ASSISTANT = "assistant"
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SYSTEM = "system"
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FUNCTION = "function"
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OBSERVATION = "observation"
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def merge_dataset(
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all_datasets: List[Union["Dataset", "IterableDataset"]],
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data_args: "DataArguments",
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training_args: "Seq2SeqTrainingArguments",
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) -> Union["Dataset", "IterableDataset"]:
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if len(all_datasets) == 1:
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return all_datasets[0]
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elif data_args.mix_strategy == "concat":
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if data_args.streaming:
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logger.warning("The samples between different datasets will not be mixed in streaming mode.")
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return concatenate_datasets(all_datasets)
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elif data_args.mix_strategy.startswith("interleave"):
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if not data_args.streaming:
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logger.warning("We recommend using `mix_strategy=concat` in non-streaming mode.")
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return interleave_datasets(
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datasets=all_datasets,
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probabilities=data_args.interleave_probs,
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seed=training_args.seed,
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stopping_strategy="first_exhausted" if data_args.mix_strategy.endswith("under") else "all_exhausted",
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)
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else:
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raise ValueError("Unknown mixing strategy.")
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def split_dataset(
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dataset: Union["Dataset", "IterableDataset"], data_args: "DataArguments", training_args: "Seq2SeqTrainingArguments"
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) -> Dict[str, "Dataset"]:
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if training_args.do_train:
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if data_args.val_size > 1e-6: # Split the dataset
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if data_args.streaming:
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dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
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val_set = dataset.take(int(data_args.val_size))
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train_set = dataset.skip(int(data_args.val_size))
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return {"train_dataset": train_set, "eval_dataset": val_set}
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else:
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val_size = int(data_args.val_size) if data_args.val_size > 1 else data_args.val_size
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dataset = dataset.train_test_split(test_size=val_size, seed=training_args.seed)
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return {"train_dataset": dataset["train"], "eval_dataset": dataset["test"]}
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else:
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if data_args.streaming:
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dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
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return {"train_dataset": dataset}
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else: # do_eval or do_predict
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return {"eval_dataset": dataset}
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