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[data] fix loader (#7207)

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* fix dataloader

* add test case

* fix type

* fix ci

* fix ci

* fix ci

* disable overwrite cache in ci

Former-commit-id: e84af0e140b1aafd1a6d6fe185a8e41c8fc5f831
This commit is contained in:
hoshi-hiyouga
2025-03-07 17:20:46 +08:00
committed by GitHub
parent 82a2bac866
commit 16419b2834
16 changed files with 161 additions and 92 deletions
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62
src/llamafactory/data/loader.py
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@@ -17,13 +17,13 @@ import sys
from typing import TYPE_CHECKING, Dict, Literal, Optional, Sequence, Union
import numpy as np
from datasets import DatasetDict, load_dataset, load_from_disk
from datasets import load_dataset, load_from_disk
from ..extras import logging
from ..extras.constants import FILEEXT2TYPE
from ..extras.misc import check_version, has_tokenized_data
from .converter import align_dataset
from .data_utils import merge_dataset, split_dataset
from .data_utils import get_dataset_module, merge_dataset, split_dataset
from .parser import get_dataset_list
from .processor import (
FeedbackDatasetProcessor,
@@ -292,23 +292,12 @@ def get_dataset(
if data_args.tokenized_path is not None:
if has_tokenized_data(data_args.tokenized_path):
logger.warning_rank0("Loading dataset from disk will ignore other data arguments.")
tokenized_data: Union["Dataset", "DatasetDict"] = load_from_disk(data_args.tokenized_path)
logger.info_rank0(f"Loaded tokenized dataset from {data_args.tokenized_path}.")
dataset_module: Dict[str, "Dataset"] = {}
if isinstance(tokenized_data, DatasetDict):
if "train" in tokenized_data:
dataset_module["train_dataset"] = tokenized_data["train"]
if "validation" in tokenized_data:
dataset_module["eval_dataset"] = tokenized_data["validation"]
else: # single dataset
dataset_module["train_dataset"] = tokenized_data
tokenized_data = load_from_disk(data_args.tokenized_path)
dataset_module = get_dataset_module(tokenized_data)
if data_args.streaming:
dataset_module = {k: v.to_iterable_dataset() for k, v in dataset_module.items()}
dataset_module["train_dataset"] = dataset_module["train_dataset"].to_iterable_dataset()
logger.info_rank0(f"Loaded tokenized dataset from {data_args.tokenized_path}.")
return dataset_module
if data_args.streaming:
@@ -335,27 +324,7 @@ def get_dataset(
eval_dataset, data_args, training_args, stage, template, tokenizer, processor, is_eval=True
)
if data_args.val_size > 1e-6:
dataset_dict = split_dataset(dataset, data_args, seed=training_args.seed)
else:
dataset_dict = {}
if dataset is not None:
if data_args.streaming:
dataset = dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
dataset_dict["train"] = dataset
if eval_dataset is not None:
if isinstance(eval_dataset, dict):
dataset_dict.update({f"validation_{name}": data for name, data in eval_dataset.items()})
else:
if data_args.streaming:
eval_dataset = eval_dataset.shuffle(buffer_size=data_args.buffer_size, seed=training_args.seed)
dataset_dict["validation"] = eval_dataset
dataset_dict = DatasetDict(dataset_dict)
dataset_dict = split_dataset(dataset, eval_dataset, data_args, seed=training_args.seed)
if data_args.tokenized_path is not None: # save tokenized dataset to disk and exit
if training_args.should_save:
dataset_dict.save_to_disk(data_args.tokenized_path)
@@ -364,19 +333,4 @@ def get_dataset(
sys.exit(0)
dataset_module = {}
if "train" in dataset_dict:
dataset_module["train_dataset"] = dataset_dict["train"]
if "validation" in dataset_dict:
dataset_module["eval_dataset"] = dataset_dict["validation"]
else:
eval_dataset = {}
for key in dataset_dict.keys():
if key.startswith("validation_"):
eval_dataset[key[len("validation_") :]] = dataset_dict[key]
if len(eval_dataset):
dataset_module["eval_dataset"] = eval_dataset
return dataset_module
return get_dataset_module(dataset_dict)