[data] support for specifying a dataset in cloud storage (#7567)

* add support for loading datasets from s3/gcs

* add comments to readme

* run linter and address comments

* add option to pass in kwargs to ray init (i.e. runtime env)

* address comment

* revert mixed up changes

README.md

@@ -554,7 +554,7 @@ pip install .
 
 ### Data Preparation
 
-Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can either use datasets on HuggingFace / ModelScope / Modelers hub or load the dataset in local disk.
+Please refer to [data/README.md](data/README.md) for checking the details about the format of dataset files. You can use datasets on HuggingFace / ModelScope / Modelers hub, load the dataset in local disk, or specify a path to s3/gcs cloud storage.
 
 > [!NOTE]
 > Please update `data/dataset_info.json` to use your custom dataset.

data/README.md

@@ -4,9 +4,10 @@ Currently we support datasets in **alpaca** and **sharegpt** format.
 
 ```json
 "dataset_name": {
-  "hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore script_url and file_name)",
-  "ms_hub_url": "the name of the dataset repository on the Model Scope hub. (if specified, ignore script_url and file_name)",
-  "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore file_name)",
+  "hf_hub_url": "the name of the dataset repository on the Hugging Face hub. (if specified, ignore script_url, file_name and cloud_file_name)",
+  "ms_hub_url": "the name of the dataset repository on the Model Scope hub. (if specified, ignore script_url, file_name and cloud_file_name)",
+  "script_url": "the name of the directory containing a dataset loading script. (if specified, ignore file_name and cloud_file_name)",
+  "cloud_file_name": "the name of the dataset file in s3/gcs cloud storage. (if specified, ignore file_name)",
   "file_name": "the name of the dataset folder or dataset file in this directory. (required if above are not specified)",
   "formatting": "the format of the dataset. (optional, default: alpaca, can be chosen from {alpaca, sharegpt})",
   "ranking": "whether the dataset is a preference dataset or not. (default: False)",

src/llamafactory/data/data_utils.py

@@ -12,9 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import json
 from enum import Enum, unique
 from typing import TYPE_CHECKING, Optional, TypedDict, Union
 
+import fsspec
 from datasets import DatasetDict, concatenate_datasets, interleave_datasets
 
 from ..extras import logging
@@ -138,3 +140,50 @@ def get_dataset_module(dataset: Union["Dataset", "DatasetDict"]) -> "DatasetModu
         dataset_module["train_dataset"] = dataset
 
     return dataset_module
+
+
+def setup_fs(path, anon=False):
+    """Set up a filesystem object based on the path protocol."""
+    storage_options = {"anon": anon} if anon else {}
+
+    if path.startswith("s3://"):
+        fs = fsspec.filesystem("s3", **storage_options)
+    elif path.startswith(("gs://", "gcs://")):
+        fs = fsspec.filesystem("gcs", **storage_options)
+    else:
+        raise ValueError(f"Unsupported protocol in path: {path}. Use 's3://' or 'gs://'")
+    return fs
+
+
+def read_cloud_json(cloud_path):
+    """Read a JSON/JSONL file from cloud storage (S3 or GCS).
+
+    Args:
+        cloud_path : str
+            Cloud path in the format:
+            - 's3://bucket-name/file.json' for AWS S3
+            - 'gs://bucket-name/file.jsonl' or 'gcs://bucket-name/file.jsonl' for Google Cloud Storage
+        lines : bool, default=True
+            If True, read the file as JSON Lines format (one JSON object per line)
+    """
+    try:
+        # Try with anonymous access first
+        fs = setup_fs(cloud_path, anon=True)
+        return _read_json_with_fs(fs, cloud_path, lines=cloud_path.endswith(".jsonl"))
+    except Exception:
+        # Try again with credentials
+        fs = setup_fs(cloud_path)
+        return _read_json_with_fs(fs, cloud_path, lines=cloud_path.endswith(".jsonl"))
+
+
+def _read_json_with_fs(fs, path, lines=True):
+    """Helper function to read JSON/JSONL files using fsspec."""
+    with fs.open(path, "r") as f:
+        if lines:
+            # Read JSONL (JSON Lines) format - one JSON object per line
+            data = [json.loads(line) for line in f if line.strip()]
+        else:
+            # Read regular JSON format
+            data = json.load(f)
+
+    return data

src/llamafactory/data/loader.py

@@ -16,13 +16,13 @@ import os
 from typing import TYPE_CHECKING, Literal, Optional, Union
 
 import numpy as np
-from datasets import load_dataset, load_from_disk
+from datasets import Dataset, 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 get_dataset_module, merge_dataset, split_dataset
+from .data_utils import get_dataset_module, merge_dataset, read_cloud_json, split_dataset
 from .parser import get_dataset_list
 from .processor import (
     FeedbackDatasetProcessor,
@@ -67,6 +67,9 @@ def _load_single_dataset(
         data_name = dataset_attr.subset
         data_dir = dataset_attr.folder
 
+    elif dataset_attr.load_from == "cloud_file":
+        data_path = dataset_attr.dataset_name
+
     elif dataset_attr.load_from == "file":
         data_files = []
         local_path = os.path.join(data_args.dataset_dir, dataset_attr.dataset_name)
@@ -122,6 +125,8 @@ def _load_single_dataset(
             token=model_args.om_hub_token,
             streaming=data_args.streaming,
         )
+    elif dataset_attr.load_from == "cloud_file":
+        dataset = Dataset.from_list(read_cloud_json(data_path), split=dataset_attr.split)
     else:
         dataset = load_dataset(
             path=data_path,

src/llamafactory/data/parser.py

@@ -141,6 +141,8 @@ def get_dataset_list(dataset_names: Optional[list[str]], dataset_dir: str) -> li
                 dataset_attr = DatasetAttr("hf_hub", dataset_name=dataset_info[name]["hf_hub_url"])
         elif "script_url" in dataset_info[name]:
             dataset_attr = DatasetAttr("script", dataset_name=dataset_info[name]["script_url"])
+        elif "cloud_file_name" in dataset_info[name]:
+            dataset_attr = DatasetAttr("cloud_file", dataset_name=dataset_info[name]["cloud_file_name"])
         else:
             dataset_attr = DatasetAttr("file", dataset_name=dataset_info[name]["file_name"])