add dataset datatypes

llama_stack/apis/dataset/dataset.py

@@ -4,46 +4,105 @@
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
 
-from typing import Any, Dict, Optional, Protocol
-
-from llama_models.llama3.api.datatypes import URL
+from abc import ABC, abstractmethod
+from enum import Enum
+from typing import Any, Dict, Generic, Iterator, Literal, Protocol, TypeVar, Union
 
 from llama_models.schema_utils import json_schema_type, webmethod
 
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
+from typing_extensions import Annotated
+
+TDatasetRow = TypeVar("TDatasetRow")
 
 
 @json_schema_type
-class TrainEvalDataset(BaseModel):
-    """Dataset to be used for training or evaluating language models."""
+class DatasetRow(BaseModel): ...
+
 
-    # unique identifier associated with the dataset
-    dataset_id: str
-    content_url: URL
-    metadata: Optional[Dict[str, Any]] = None
+@json_schema_type
+class DictSample(DatasetRow):
+    data: Dict[str, Any]
 
 
 @json_schema_type
-class CreateDatasetRequest(BaseModel):
-    """Request to create a dataset."""
+class Generation(BaseModel): ...
+
 
-    uuid: str
-    dataset: TrainEvalDataset
+@json_schema_type
+class DatasetType(Enum):
+    custom = "custom"
+    huggingface = "huggingface"
+
+
+@json_schema_type
+class HuggingfaceDatasetDef(BaseModel):
+    type: Literal[DatasetType.huggingface.value] = DatasetType.huggingface.value
+    identifier: str = Field(
+        description="A unique name for the dataset",
+    )
+    dataset_name: str = Field(
+        description="The name of the dataset into HF (e.g. hellawag)",
+    )
+    kwargs: Dict[str, Any] = Field(
+        description="Any additional arguments to get Huggingface (e.g. split, trust_remote_code)",
+        default_factory=dict,
+    )
+
+
+@json_schema_type
+class CustomDatasetDef(BaseModel):
+    type: Literal[DatasetType.custom.value] = DatasetType.custom.value
+    identifier: str = Field(
+        description="A unique name for the dataset",
+    )
+    url: str = Field(
+        description="The URL to the dataset",
+    )
+
+
+DatasetDef = Annotated[
+    Union[
+        HuggingfaceDatasetDef,
+        CustomDatasetDef,
+    ],
+    Field(discriminator="type"),
+]
+
+
+class BaseDataset(ABC, Generic[TDatasetRow]):
+    def __init__(self) -> None:
+        self.type: str = self.__class__.__name__
+
+    @abstractmethod
+    def __iter__(self) -> Iterator[TDatasetRow]:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def load(self) -> None:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def __str__(self) -> str:
+        raise NotImplementedError()
+
+    @abstractmethod
+    def __len__(self) -> int:
+        raise NotImplementedError()
 
 
 class Datasets(Protocol):
     @webmethod(route="/datasets/create")
     def create_dataset(
         self,
-        uuid: str,
-        dataset: TrainEvalDataset,
+        dataset: DatasetDef,
     ) -> None: ...
 
     @webmethod(route="/datasets/get")
     def get_dataset(
         self,
-        dataset_uuid: str,
-    ) -> TrainEvalDataset: ...
+        dataset_identifier: str,
+    ) -> DatasetDef: ...
 
     @webmethod(route="/datasets/delete")
     def delete_dataset(

llama_stack/apis/evals/evals.py

@@ -33,6 +33,7 @@ class EvaluateTaskConfig(BaseModel):
 class EvaluateResponse(BaseModel):
     """Scores for evaluation."""
 
+    preprocess_output: GenerationOutput
     metrics: Dict[str, str]
 
 

llama_stack/distribution/registry/datasets/__init__.py

@@ -5,19 +5,19 @@
 # the root directory of this source tree.
 
 # TODO: make these import config based
-from .dataset import CustomDataset, HFDataset
-from .dataset_registry import DatasetRegistry
+# from .dataset import CustomDataset, HFDataset
+# from .dataset_registry import DatasetRegistry
 
-DATASETS_REGISTRY = {
-    "mmlu-simple-eval-en": CustomDataset(
-        name="mmlu_eval",
-        url="https://openaipublic.blob.core.windows.net/simple-evals/mmlu.csv",
-    ),
-    "hellaswag": HFDataset(
-        name="hellaswag",
-        url="hf://hellaswag?split=validation&trust_remote_code=True",
-    ),
-}
+# DATASETS_REGISTRY = {
+#     "mmlu-simple-eval-en": CustomDataset(
+#         name="mmlu_eval",
+#         url="https://openaipublic.blob.core.windows.net/simple-evals/mmlu.csv",
+#     ),
+#     "hellaswag": HFDataset(
+#         name="hellaswag",
+#         url="hf://hellaswag?split=validation&trust_remote_code=True",
+#     ),
+# }
 
-for k, v in DATASETS_REGISTRY.items():
-    DatasetRegistry.register(k, v)
+# for k, v in DATASETS_REGISTRY.items():
+#     DatasetRegistry.register(k, v)

llama_stack/distribution/registry/datasets/dataset.py

@@ -3,60 +3,88 @@
 #
 # This source code is licensed under the terms described in the LICENSE file in
 # the root directory of this source tree.
-
-from abc import ABC, abstractmethod
-from urllib.parse import parse_qs, urlparse
-
 import pandas
 from datasets import Dataset, load_dataset
 
+from llama_stack.apis.dataset import *  # noqa: F403
+
 
-class BaseDataset(ABC):
-    def __init__(self, name: str):
+class CustomDataset(BaseDataset[DictSample]):
+    def __init__(self, config: CustomDatasetDef) -> None:
+        super().__init__()
+        self.config = config
         self.dataset = None
-        self.dataset_id = name
-        self.type = self.__class__.__name__
+        self.index = 0
 
-    def __iter__(self):
-        return iter(self.dataset)
+    def __iter__(self) -> Iterator[DictSample]:
+        return self
 
-    @abstractmethod
-    def load(self):
-        pass
+    def __next__(self) -> DictSample:
+        if not self.dataset:
+            self.load()
+        if self.index >= len(self.dataset):
+            raise StopIteration
+        sample = DictSample(data=self.dataset[self.index])
+        self.index += 1
+        return sample
 
+    def __str__(self):
+        return f"CustomDataset({self.config})"
 
-class CustomDataset(BaseDataset):
-    def __init__(self, name, url):
-        super().__init__(name)
-        self.url = url
+    def __len__(self):
+        if not self.dataset:
+            self.load()
+        return len(self.dataset)
 
     def load(self):
         if self.dataset:
             return
         # TODO: better support w/ data url
-        if self.url.endswith(".csv"):
-            df = pandas.read_csv(self.url)
-        elif self.url.endswith(".xlsx"):
-            df = pandas.read_excel(self.url)
+        if self.config.url.endswith(".csv"):
+            df = pandas.read_csv(self.config.url)
+        elif self.config.url.endswith(".xlsx"):
+            df = pandas.read_excel(self.config.url)
 
         self.dataset = Dataset.from_pandas(df)
 
 
-class HFDataset(BaseDataset):
-    def __init__(self, name, url):
-        super().__init__(name)
-        self.url = url
+class HuggingfaceDataset(BaseDataset[DictSample]):
+    def __init__(self, config: HuggingfaceDatasetDef):
+        super().__init__()
+        self.config = config
+        self.dataset = None
+        self.index = 0
+
+    def __iter__(self) -> Iterator[DictSample]:
+        return self
+
+    def __next__(self) -> DictSample:
+        if not self.dataset:
+            self.load()
+        if self.index >= len(self.dataset):
+            raise StopIteration
+        sample = DictSample(data=self.dataset[self.index])
+        self.index += 1
+        return sample
+
+    def __str__(self):
+        return f"HuggingfaceDataset({self.config})"
+
+    def __len__(self):
+        if not self.dataset:
+            self.load()
+        return len(self.dataset)
 
     def load(self):
         if self.dataset:
             return
+        self.dataset = load_dataset(self.config.dataset_name, **self.config.kwargs)
+        # parsed = urlparse(self.url)
 
-        parsed = urlparse(self.url)
-
-        if parsed.scheme != "hf":
-            raise ValueError(f"Unknown HF dataset: {self.url}")
+        # if parsed.scheme != "hf":
+        #     raise ValueError(f"Unknown HF dataset: {self.url}")
 
-        query = parse_qs(parsed.query)
-        query = {k: v[0] for k, v in query.items()}
-        path = parsed.netloc
-        self.dataset = load_dataset(path, **query)
+        # query = parse_qs(parsed.query)
+        # query = {k: v[0] for k, v in query.items()}
+        # path = parsed.netloc
+        # self.dataset = load_dataset(path, **query)

llama_stack/distribution/registry/datasets/dataset_registry.py

@@ -5,7 +5,7 @@
 # the root directory of this source tree.
 from typing import AbstractSet, Dict
 
-from .dataset import BaseDataset
+from llama_stack.apis.dataset import BaseDataset
 
 
 class DatasetRegistry: