Convert single data to list

Signed-off-by: Samuel Monson <[email protected]>

Author: sjmonson

src/guidellm/benchmark/schemas.py

@@ -23,7 +23,15 @@
 from typing import Any, ClassVar, Literal, TypeVar, cast
 
 import yaml
-from pydantic import ConfigDict, Field, computed_field, model_serializer
+from pydantic import (
+    ConfigDict,
+    Field,
+    ValidationError,
+    ValidatorFunctionWrapHandler,
+    computed_field,
+    field_validator,
+    model_serializer,
+)
 from torch.utils.data import Sampler
 from transformers import PreTrainedTokenizerBase
 
@@ -1142,7 +1150,8 @@ def update_estimate(
         )
         request_duration = (
             (request_end_time - request_start_time)
-            if request_end_time and request_start_time else None
+            if request_end_time and request_start_time
+            else None
         )
 
         # Always track concurrency
@@ -1818,8 +1827,6 @@ def get_default(cls: type[BenchmarkGenerativeTextArgs], field: str) -> Any:
         else:
             return factory({})  # type: ignore[call-arg] # Confirmed correct at runtime by code above
 
-
-
     model_config = ConfigDict(
         extra="ignore",
         use_enum_values=True,
@@ -1931,6 +1938,26 @@ def get_default(cls: type[BenchmarkGenerativeTextArgs], field: str) -> Any:
         default=None, description="Maximum global error rate (0-1) before stopping"
     )
 
+    @field_validator("data", mode="wrap")
+    @classmethod
+    def single_to_list(
+        cls, value: Any, handler: ValidatorFunctionWrapHandler
+    ) -> list[Any]:
+        """
+        Ensures 'data' field is always a list.
+
+        :param value: Input value for the 'data' field
+        :return: List of data sources
+        """
+        try:
+            return handler(value)
+        except ValidationError as err:
+            # If validation fails, try wrapping the value in a list
+            if err.errors()[0]["type"] == "list_type":
+                return handler([value])
+            else:
+                raise
+
     @model_serializer
     def serialize_model(self):
         """