Better scenario from-file support (#432)

## Summary

<!--
Include a short paragraph of the changes introduced in this PR.
If this PR requires additional context or rationale, explain why
the changes are necessary.
-->
Allows for scenarios to pass yaml or json formatted synthetic config to
data. Automatically handles conversion from single data to list.

```yaml
---
target: "http://vllm-deployment.kserve-e2e-perf.svc.cluster.local:8000/"
profile: concurrent
rate: [1,2,4,8]
max_seconds: 120
data:
  prompt_tokens_min: 10
  prompt_tokens_max: 8192
  prompt_tokens: 4096
  prompt_tokens_stdev: 2048
  output_tokens_min: 10
  output_tokens_max: 2048
  output_tokens: 1024
  output_tokens_stdev: 512
```

---

- [x] "I certify that all code in this PR is my own, except as noted
below."

## Use of AI

- [x] Includes AI-assisted code completion
- [ ] Includes code generated by an AI application
- [ ] Includes AI-generated tests (NOTE: AI written tests should have a
docstring that includes `## WRITTEN BY AI ##`)

Author: sjmonson

src/guidellm/__main__.py

@@ -33,7 +33,7 @@
 try:
     import uvloop
 except ImportError:
-    uvloop = None # type: ignore[assignment] # Optional dependency
+    uvloop = None  # type: ignore[assignment] # Optional dependency
 
 from guidellm.backends import BackendType
 from guidellm.benchmark import (
@@ -116,6 +116,7 @@ def benchmark():
 )
 @click.option(
     "--scenario",
+    "-c",
     type=cli_tools.Union(
         click.Path(
             exists=True,
@@ -392,8 +393,10 @@ def run(**kwargs):
     disable_progress = kwargs.pop("disable_progress", False)
 
     try:
+        # Only set CLI args that differ from click defaults
+        new_kwargs = cli_tools.set_if_not_default(click.get_current_context(), **kwargs)
         args = BenchmarkGenerativeTextArgs.create(
-            scenario=kwargs.pop("scenario", None), **kwargs
+            scenario=new_kwargs.pop("scenario", None), **new_kwargs
         )
     except ValidationError as err:
         # Translate pydantic valdation error to click argument error

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
@@ -1669,7 +1678,7 @@ def compile(
         estimated_state: EstimatedBenchmarkState,
         scheduler_state: SchedulerState,
         profile: Profile,
-        requests: Iterable,
+        requests: Iterable,  # noqa: ARG003
         backend: BackendInterface,
         environment: Environment,
         strategy: SchedulingStrategy,
@@ -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,
@@ -1838,7 +1845,7 @@ def get_default(cls: type[BenchmarkGenerativeTextArgs], field: str) -> Any:
     profile: StrategyType | ProfileType | Profile = Field(
         default="sweep", description="Benchmark profile or scheduling strategy type"
     )
-    rate: float | list[float] | None = Field(
+    rate: list[float] | None = Field(
         default=None, description="Request rate(s) for rate-based scheduling"
     )
     # Backend configuration
@@ -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", "data_args", "rate", mode="wrap")
+    @classmethod
+    def single_to_list(
+        cls, value: Any, handler: ValidatorFunctionWrapHandler
+    ) -> list[Any]:
+        """
+        Ensures 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):
         """

src/guidellm/data/deserializers/synthetic.py

@@ -9,7 +9,7 @@
 import yaml
 from datasets import Features, IterableDataset, Value
 from faker import Faker
-from pydantic import ConfigDict, Field, model_validator
+from pydantic import ConfigDict, Field, ValidationError, model_validator
 from transformers import PreTrainedTokenizerBase
 
 from guidellm.data.deserializers.deserializer import (
@@ -242,6 +242,10 @@ def __call__(
         if (config := self._load_config_str(data)) is not None:
             return self(config, processor_factory, random_seed, **data_kwargs)
 
+        # Try to parse dict-like data directly
+        if (config := self._load_config_dict(data)) is not None:
+            return self(config, processor_factory, random_seed, **data_kwargs)
+
         if not isinstance(data, SyntheticTextDatasetConfig):
             raise DataNotSupportedError(
                 "Unsupported data for SyntheticTextDatasetDeserializer, "
@@ -266,6 +270,15 @@ def __call__(
             ),
         )
 
+    def _load_config_dict(self, data: Any) -> SyntheticTextDatasetConfig | None:
+        if not isinstance(data, dict | list):
+            return None
+
+        try:
+            return SyntheticTextDatasetConfig.model_validate(data)
+        except ValidationError:
+            return None
+
     def _load_config_file(self, data: Any) -> SyntheticTextDatasetConfig | None:
         if (not isinstance(data, str) and not isinstance(data, Path)) or (
             not Path(data).is_file()