[data] add new reason tool calls demo data (#9249)

Co-authored-by: unknown <Peter Zeng>
Co-authored-by: Yaowei Zheng <hiyouga@buaa.edu.cn>

.github/workflows/tests.yml

@@ -43,6 +43,9 @@ jobs:
           - python: "3.9"
             os: "ubuntu-latest"
             transformers: "4.53.0"
+        exclude:  # exclude python 3.9 on macos
+          - python: "3.9"
+            os: "macos-latest"
 
     runs-on: ${{ matrix.os }}
 

src/llamafactory/v1/core/base_trainer.py

@@ -12,8 +12,25 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from typing import Any
+
 from ..config.training_args import TrainingArguments
-from ..extras.types import DataCollator, Model, Processor, TorchDataset
+from ..extras.types import Model, Processor, Tensor, TorchDataset
+
+
+class DataCollator:
+    """Default Data collator."""
+
+    def __init__(self, processor: Processor) -> None:
+        self.processor = processor
+
+    def __call__(self, features: list[dict[str, Any]]) -> dict[str, Tensor]:
+        """Collate features into a batch."""
+        for feature in features:
+            pass
+
+        # sft: messages
+        # dpo: chosen_messages, rejected_messages
 
 
 class BaseTrainer:

src/llamafactory/v1/core/data_engine.py

@@ -22,19 +22,7 @@ from omegaconf import OmegaConf
 from torch.utils.data import Dataset
 
 from ..config.data_args import DataArguments
-from ..extras.types import DatasetInfo, HFDataset, Processor, Tensor
-from ..plugins.data_plugins.loader import DataGetItemPlugin, DataIndexPlugin, DataLoaderPlugin
-
-
-class DataCollator:
-    """Default Data collator."""
-
-    def __init__(self, processor: Processor, dataset_info: dict[str, DatasetInfo]) -> None:
-        self.processor = processor
-        self.dataset_info = dataset_info
-
-    def __call__(self, features: list[dict[str, Any]]) -> dict[str, Tensor]:
-        pass
+from ..extras.types import DatasetInfo, HFDataset, Sample
 
 
 class DataEngine(Dataset):
@@ -45,73 +33,78 @@ class DataEngine(Dataset):
         """Data arguments."""
         self.datasets: dict[str, HFDataset] = {}
         """Dict of (dataset_name, dataset)"""
-        self.dataset_info: dict[str, DatasetInfo] = {}
+        self.dataset_infos: dict[str, DatasetInfo] = {}
         """Dict of (dataset_name, dataset_info)"""
-        self.streaming: bool = False
-        """Whether dataset is streaming."""
         self.data_index: list[tuple[str, int]] = []
         """List of (dataset_name, sample_index)"""
-        self.data_loader_plugin = DataLoaderPlugin(args=self.args)
-        """Data loader plugin."""
-        self.data_index_plugin = DataIndexPlugin()
-        """Data index plugin."""
-        self.data_getitem_plugin = DataGetItemPlugin(datasets=self.datasets, data_index=self.data_index)
-        """Data getitem plugin."""
+        self.streaming: bool = False
+        """Whether dataset is streaming."""
         self.get_dataset_info()
         self.load_dataset()
         self.build_data_index()
 
     def get_dataset_info(self) -> None:
-        """Get dataset info."""
+        """Get dataset info from data arguments."""
         if self.args.dataset.endswith(".yaml") and os.path.isfile(
             os.path.join(self.args.dataset_dir, self.args.dataset)
         ):  # local file
-            self.dataset_info = OmegaConf.load(os.path.join(self.args.dataset_dir, self.args.dataset))
+            self.dataset_infos = OmegaConf.load(os.path.join(self.args.dataset_dir, self.args.dataset))
         elif self.args.dataset.endswith(".yaml"):  # hf hub uri, e.g. llamafactory/v1-sft-demo/dataset_info.yaml
             repo_id, filename = os.path.split(self.args.dataset)
             filepath = hf_hub_download(repo_id=repo_id, filename=filename, repo_type="dataset")
-            self.dataset_info = OmegaConf.load(filepath)
+            self.dataset_infos = OmegaConf.load(filepath)
         elif os.path.exists(os.path.join(self.args.dataset_dir, self.args.dataset)):  # local file(s)
-            self.dataset_info = {"default": {"file_name": self.args.dataset}}
+            self.dataset_infos = {"default": {"file_name": self.args.dataset}}
         else:  # hf hub dataset, e.g. llamafactory/v1-sft-demo
-            self.dataset_info = {"default": {"hf_hub_url": self.args.dataset}}
+            self.dataset_infos = {"default": {"hf_hub_url": self.args.dataset}}
 
     def load_dataset(self) -> None:
-        """Load dataset from dataset info."""
-        for key, value in self.dataset_info.items():
+        """Load datasets according to dataset info."""
+        for key, value in self.dataset_infos.items():
             split = value.get("split", "train")
             streaming = value.get("streaming", False)
             self.streaming |= streaming
             if "hf_hub_url" in value:
                 self.datasets[key] = load_dataset(value["hf_hub_url"], split=split, streaming=streaming)
             else:  # data loader plugin
-                self.datasets[key] = self.data_loader_plugin.auto_load_data(value)
+                from ..plugins.data_plugins.loader import DataLoaderPlugin
+
+                self.datasets[key] = DataLoaderPlugin(args=self.args).auto_load_data(value)
 
     def build_data_index(self) -> None:
         """Build dataset index."""
         for dataset_name, dataset in self.datasets.items():
-            size = self.dataset_info[dataset_name].get("size")
-            weight = self.dataset_info[dataset_name].get("weight")
+            size = self.dataset_infos[dataset_name].get("size")
+            weight = self.dataset_infos[dataset_name].get("weight")
             if self.streaming:
                 data_index = [(dataset_name, -1) for _ in range(1000)]
             else:
                 data_index = [(dataset_name, sample_index) for sample_index in range(len(dataset))]
 
             if size or weight:  # data index plugin
-                data_index = self.data_index_plugin.adjust_data_index(data_index, size, weight)
+                from ..plugins.data_plugins.loader import DataIndexPlugin
+
+                data_index = DataIndexPlugin().adjust_data_index(data_index, size, weight)
 
             self.data_index.extend(data_index)
 
-    def get_data_collator(self, processor: Processor) -> DataCollator:
-        """Get data collator.
+    def _convert_data_sample(self, raw_sample: dict[str, Any], dataset_name: str) -> Sample:
+        """Convert dataset sample.
 
         Args:
-            processor (Processor): Processor.
+            raw_sample (dict[str, Any]): Raw dataset sample.
+            dataset_name (str): Dataset name.
 
         Returns:
-            DataCollator: Data collator.
+            Sample: Dataset sample.
         """
-        return DataCollator(processor=processor, dataset_info=self.dataset_info)
+        converter = self.dataset_infos[dataset_name].get("converter")
+        if converter is not None:
+            from ..plugins.data_plugins.converter import get_converter
+
+            return {"_dataset_name": dataset_name, **get_converter(converter)(raw_sample)}
+        else:
+            return {"_dataset_name": dataset_name, **raw_sample}
 
     def __len__(self) -> int:
         """Get dataset length.
@@ -124,23 +117,33 @@ class DataEngine(Dataset):
         else:
             return len(self.data_index)
 
-    def __getitem__(self, index: Union[int, slice, list[int]]) -> Union[dict, list[dict]]:
+    def __getitem__(self, index: Union[int, Any]) -> Union[Sample, list[Sample]]:
         """Get dataset item.
 
         Args:
             index (int): Dataset index.
 
         Returns:
-            dict: Dataset item.
+            Sample: Dataset item.
         """
         if self.streaming:
             raise ValueError("Streaming dataset does not support index access.")
 
         if isinstance(index, int):
             dataset_name, sample_index = self.data_index[index]
-            return {"_dataset_name": dataset_name, **self.datasets[dataset_name][sample_index]}
+            return self._convert_data_sample(self.datasets[dataset_name][sample_index], dataset_name)
         else:
-            return self.data_getitem_plugin.get_data(index)
+            from ..plugins.data_plugins.loader import DataSelectorPlugin
+
+            selected_index = DataSelectorPlugin(data_index=self.data_index).select(index)
+            if isinstance(selected_index, list):
+                return [
+                    self._convert_data_sample(self.datasets[dataset_name][sample_index], dataset_name)
+                    for dataset_name, sample_index in selected_index
+                ]
+            else:
+                dataset_name, sample_index = selected_index
+                return self._convert_data_sample(self.datasets[dataset_name][sample_index], dataset_name)
 
     def __iter__(self) -> Iterable:
         """Get dataset iterator.
@@ -156,7 +159,7 @@ class DataEngine(Dataset):
 
         raise NotImplementedError()
 
-    def __aiter__(self) -> AsyncIterable:
+    async def __aiter__(self) -> AsyncIterable:
         """Get dataset async iterator.
 
         Returns:
@@ -169,3 +172,11 @@ class DataEngine(Dataset):
             pass
 
         raise NotImplementedError()
+
+
+if __name__ == "__main__":
+    from ..config.parser import get_args
+
+    data_args, *_ = get_args()
+    data_engine = DataEngine(data_args=data_args)
+    print(data_engine[0])

src/llamafactory/v1/extras/types.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from typing import TYPE_CHECKING, NotRequired, TypedDict, Union
+from typing import TYPE_CHECKING, Literal, NotRequired, TypedDict, Union
 
 
 if TYPE_CHECKING:
@@ -26,7 +26,8 @@ if TYPE_CHECKING:
     HFDataset = Union[datasets.Dataset, datasets.IterableDataset]
     DataCollator = transformers.DataCollator
     DataLoader = torch.utils.data.DataLoader
-    Model = transformers.PreTrainedModel
+    HFModel = transformers.PreTrainedModel
+    DistModel = torch.nn.parallel.DistributedDataParallel
     Processor = Union[transformers.PreTrainedTokenizer, transformers.ProcessorMixin]
 else:
     Tensor = None
@@ -34,7 +35,8 @@ else:
     HFDataset = None
     DataCollator = None
     DataLoader = None
-    Model = None
+    HFModel = None
+    DistModel = None
     Processor = None
 
 
@@ -55,3 +57,37 @@ class DatasetInfo(TypedDict, total=False):
     """Dataset weight, default to 1.0."""
     streaming: NotRequired[bool]
     """Is streaming dataset, default to False."""
+
+
+class Content(TypedDict):
+    type: Literal["text", "tools", "reasoning", "tool_calls", "image_url"]
+    value: str
+
+
+class Message(TypedDict):
+    role: Literal["system", "user", "assistant"]
+    content: list[Content]
+    loss_weight: float
+
+
+class SFTSample(TypedDict):
+    messages: list[Message]
+    extra_info: NotRequired[str]
+    _dataset_name: NotRequired[str]
+
+
+class DPOSample(TypedDict):
+    chosen_messages: list[Message]
+    rejected_messages: list[Message]
+    extra_info: NotRequired[str]
+    _dataset_name: NotRequired[str]
+
+
+Sample = Union[SFTSample, DPOSample]
+
+
+class Model(TypedDict):
+    hf_model: HFModel
+    """HF model."""
+    dist_model: DistModel
+    """Distributed model."""

src/llamafactory/v1/plugins/data_plugins/converter.py

@@ -0,0 +1,71 @@
+# Copyright 2025 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 typing import Callable, NotRequired, TypedDict
+
+from ...extras.types import Sample, SFTSample
+
+
+class AlpacaSample(TypedDict, total=False):
+    system: NotRequired[str]
+    instruction: NotRequired[str]
+    input: NotRequired[str]
+    output: NotRequired[str]
+
+
+def alpaca_converter(raw_sample: AlpacaSample) -> SFTSample:
+    """Convert Alpaca sample to SFT sample.
+
+    Args:
+        raw_sample (AlpacaSample): Alpaca sample.
+
+    Returns:
+        SFTSample: SFT sample.
+    """
+    messages = []
+    if "system" in raw_sample:
+        messages.append(
+            {"role": "system", "content": [{"type": "text", "value": raw_sample["system"]}], "loss_weight": 0.0}
+        )
+
+    if "instruction" in raw_sample or "input" in raw_sample:
+        messages.append(
+            {
+                "role": "user",
+                "content": [
+                    {"type": "text", "value": raw_sample.get("instruction", "") + raw_sample.get("input", "")}
+                ],
+                "loss_weight": 0.0,
+            }
+        )
+
+    if "output" in raw_sample:
+        messages.append(
+            {"role": "assistant", "content": [{"type": "text", "value": raw_sample["output"]}], "loss_weight": 1.0}
+        )
+
+    return {"messages": messages}
+
+
+CONVERTERS = {
+    "alpaca": alpaca_converter,
+}
+
+
+def get_converter(converter_name: str) -> Callable[[dict], Sample]:
+    if converter_name not in CONVERTERS:
+        raise ValueError(f"Converter {converter_name} not found.")
+
+    return CONVERTERS[converter_name]

src/llamafactory/v1/plugins/data_plugins/loader.py

@@ -15,7 +15,7 @@
 
 import os
 from dataclasses import dataclass
-from typing import Literal, Optional, Union
+from typing import Any, Literal, Optional, Union
 
 from datasets import load_dataset
 
@@ -25,7 +25,10 @@ from ...extras.types import DatasetInfo, HFDataset
 
 @dataclass
 class DataLoaderPlugin:
+    """Plugin for loading dataset."""
+
     args: DataArguments
+    """Data arguments."""
 
     def _get_builder_name(self, path: str) -> Literal["arrow", "csv", "json", "parquet", "text"]:
         """Get dataset builder name.
@@ -66,9 +69,21 @@ class DataLoaderPlugin:
 
 @dataclass
 class DataIndexPlugin:
+    """Plugin for adjusting dataset index."""
+
     def adjust_data_index(
         self, data_index: list[tuple[str, int]], size: Optional[int], weight: Optional[float]
     ) -> list[tuple[str, int]]:
+        """Adjust dataset index by size and weight.
+
+        Args:
+            data_index (list[tuple[str, int]]): List of (dataset_name, sample_index).
+            size (Optional[int]): Desired dataset size.
+            weight (Optional[float]): Desired dataset weight.
+
+        Returns:
+            list[tuple[str, int]]: Adjusted dataset index.
+        """
         if size is not None:
             data_index = self.adjust_by_size(data_index, size)
 
@@ -85,18 +100,24 @@ class DataIndexPlugin:
 
 
 @dataclass
-class DataGetItemPlugin:
-    datasets: dict[str, HFDataset]
+class DataSelectorPlugin:
+    """Plugin for selecting dataset samples."""
+
     data_index: list[tuple[str, int]]
+    """List of (dataset_name, sample_index)"""
 
-    def _get_by_index(self, index: int) -> dict:
-        dataset_name, sample_index = self.data_index[index]
-        return {"_dataset_name": dataset_name, **self.datasets[dataset_name][sample_index]}
+    def select(self, index: Union[slice, list[int], Any]) -> Union[tuple[str, int], list[tuple[str, int]]]:
+        """Select dataset samples.
 
-    def get_data(self, index: Union[slice, list[int]]) -> list[dict]:
+        Args:
+            index (Union[slice, list[int], Any]): Index of dataset samples.
+
+        Returns:
+            Union[tuple[str, int], list[tuple[str, int]]]: Selected dataset samples.
+        """
         if isinstance(index, slice):
-            return [self._get_by_index(i) for i in range(*index.indices(len(self.data_index)))]
+            return [self.data_index[i] for i in range(*index.indices(len(self.data_index)))]
         elif isinstance(index, list):
-            return [self._get_by_index(i) for i in index]
+            return [self.data_index[i] for i in index]
         else:
             raise ValueError(f"Invalid index type {type(index)}.")

tests_v1/core/test_data_engine.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import random
 
 import pytest
@@ -22,14 +21,11 @@ from llamafactory.v1.config.data_args import DataArguments
 from llamafactory.v1.core.data_engine import DataEngine
 
 
-TINY_DATA = os.getenv("TINY_DATA", "llamafactory/v1-sft-demo")
-
-
 @pytest.mark.parametrize("num_samples", [16])
 def test_map_dataset(num_samples: int):
-    data_args = DataArguments(dataset=TINY_DATA)
+    data_args = DataArguments(dataset="llamafactory/v1-sft-demo")
     data_engine = DataEngine(data_args)
-    original_data = load_dataset(TINY_DATA, split="train")
+    original_data = load_dataset("llamafactory/v1-sft-demo", split="train")
     indexes = random.choices(range(len(data_engine)), k=num_samples)
     for index in indexes:
         print(data_engine[index])

tests_v1/plugins/data_plugins/test_converter.py

@@ -0,0 +1,52 @@
+# Copyright 2025 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.
+
+import random
+
+import pytest
+from datasets import load_dataset
+
+from llamafactory.v1.config.data_args import DataArguments
+from llamafactory.v1.core.data_engine import DataEngine
+
+
+@pytest.mark.parametrize("num_samples", [16])
+def test_alpaca_converter(num_samples: int):
+    data_args = DataArguments(dataset="llamafactory/v1-sft-demo/dataset_info.yaml")
+    data_engine = DataEngine(data_args)
+    original_data = load_dataset("llamafactory/tiny-supervised-dataset", split="train")
+    indexes = random.choices(range(len(data_engine)), k=num_samples)
+    for index in indexes:
+        print(data_engine[index])
+        expected_data = {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "value": original_data[index]["instruction"] + original_data[index]["input"]}
+                    ],
+                    "loss_weight": 0.0,
+                },
+                {
+                    "role": "assistant",
+                    "content": [{"type": "text", "value": original_data[index]["output"]}],
+                    "loss_weight": 1.0,
+                },
+            ]
+        }
+        assert data_engine[index] == {"_dataset_name": "tiny_dataset", **expected_data}
+
+
+if __name__ == "__main__":
+    test_alpaca_converter(1)